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The effects of lockdown during the COVID-19 pandemic on fetal movement profiles (2024)
Journal Article
Reissland, N., Ustun, B., & Einbeck, J. (2024). The effects of lockdown during the COVID-19 pandemic on fetal movement profiles. BMC Pregnancy and Childbirth, 24(1), 56. https://doi.org/10.1186/s12884-024-06259-8

Purpose: The current study investigated the direct impact of the COVID-19 lockdown on fetal movements, addressing a critical research gap. While previous research has predominantly examined the effects of lockdown on maternal health and postnatal out... Read More about The effects of lockdown during the COVID-19 pandemic on fetal movement profiles.

A Versatile Model for Clustered and Highly Correlated Multivariate Data (2024)
Journal Article
Zhang, Y., & Einbeck, J. (2024). A Versatile Model for Clustered and Highly Correlated Multivariate Data. Journal of statistical theory and practice, 18(1), Article 5. https://doi.org/10.1007/s42519-023-00357-0

For the analysis of multivariate data with an approximately one-dimensional latent structure, it is suggested to model this latent variable by a random effect, allowing for the use of mixed model methodology for dimension reduction purposes. We imple... Read More about A Versatile Model for Clustered and Highly Correlated Multivariate Data.

A fresh look at mean-shift based modal clustering (2023)
Journal Article
Ameijeiras-Alonso, J., & Einbeck, J. (2023). A fresh look at mean-shift based modal clustering. Advances in Data Analysis and Classification, https://doi.org/10.1007/s11634-023-00575-1

Modal clustering is an unsupervised learning technique where cluster centers are identified as the local maxima of nonparametric probability density estimates. A natural algorithmic engine for the computation of these maxima is the mean shift procedu... Read More about A fresh look at mean-shift based modal clustering.

A Sequential Cross-Sectional Analysis Producing Robust Weekly COVID-19 Rates for South East Asian Countries (2023)
Journal Article
Almohaimeed, A., & Einbeck, J. (2023). A Sequential Cross-Sectional Analysis Producing Robust Weekly COVID-19 Rates for South East Asian Countries. Viruses, 15(7), Article 1572. https://doi.org/10.3390/v15071572

The COVID-19 pandemic has expanded fast over the world, affecting millions of people and generating serious health, social, and economic consequences. All South East Asian countries have experienced the pandemic, with various degrees of intensity and... Read More about A Sequential Cross-Sectional Analysis Producing Robust Weekly COVID-19 Rates for South East Asian Countries.

Use of a targeted, computer/web-based guided self-help psychoeducation toolkit for distressing hallucinations (MUSE) in people with an at-risk mental state for psychosis: protocol for a randomised controlled feasibility trial (2023)
Journal Article
Hamilton, J., Arnott, B., Aynsworth, C., Barclay, N. A., Birkett, L., Brandon, T., …Fernyhough, C. (2023). Use of a targeted, computer/web-based guided self-help psychoeducation toolkit for distressing hallucinations (MUSE) in people with an at-risk mental state for psychosis: protocol for a randomised controlled feasibility trial. BMJ Open, 13(6), Article e076101. https://doi.org/10.1136/bmjopen-2023-076101

Individuals who access at-risk mental state (ARMS) services often have unusual sensory experiences and levels of distress that lead them to seek help. The Managing Unusual Sensory Experiences (MUSE) treatment is a brief symptom targeted intervention... Read More about Use of a targeted, computer/web-based guided self-help psychoeducation toolkit for distressing hallucinations (MUSE) in people with an at-risk mental state for psychosis: protocol for a randomised controlled feasibility trial.

Cumulant-Based Goodness-of-Fit Tests for the Tweedie, Bar-Lev and Enis Class of Distributions (2023)
Journal Article
Bar-Lev, S. K., Batsidis, A., Einbeck, J., Liu, X., & Ren, P. (2023). Cumulant-Based Goodness-of-Fit Tests for the Tweedie, Bar-Lev and Enis Class of Distributions. Mathematics, 11(7), Article 1603. https://doi.org/10.3390/math11071603

The class of natural exponential families (NEFs) of distributions having power variance functions (NEF-PVFs) is huge (uncountable), with enormous applications in various fields. Based on a characterization property that holds for the cumulants of the... Read More about Cumulant-Based Goodness-of-Fit Tests for the Tweedie, Bar-Lev and Enis Class of Distributions.

Association between Hyperemesis Gravidarum in pregnancy on postnatal ability of infants to attend to a play task with their mother (2023)
Journal Article
Reissland, N., Matthewson, J., & Einbeck, J. (2023). Association between Hyperemesis Gravidarum in pregnancy on postnatal ability of infants to attend to a play task with their mother. Infant Behavior & Development, 71, https://doi.org/10.1016/j.infbeh.2023.101823

Research indicates a higher prevalence of attention deficits in children exposed to HG in utero compared to controls with some claiming that the deficit is due to prenatal effects of malnutrition in HG mothers and others that it is due to maternal me... Read More about Association between Hyperemesis Gravidarum in pregnancy on postnatal ability of infants to attend to a play task with their mother.

Biodose Tools: an R shiny application for biological dosimetry (2023)
Journal Article
Hernández, A., Endesfelder, D., Einbeck, J., Puig, P., Benadjaoud, M. A., Higueras, M., …Barquinero, J. F. (2023). Biodose Tools: an R shiny application for biological dosimetry. International Journal of Radiation Biology, 99(9), https://doi.org/10.1080/09553002.2023.2176564

Introduction: In the event of a radiological accident or incident, the aim of biological dosimetry is to convert the yield of a specific biomarker of exposure to ionizing radiation into an absorbed dose. Since the 1980s, various tools have been used... Read More about Biodose Tools: an R shiny application for biological dosimetry.

Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data (2022)
Journal Article
Almohaimeed, A., Einbeck, J., Qarmalah, N., & Alkhidhr, H. (2022). Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data. International Journal of Environmental Research and Public Health, 19(22), https://doi.org/10.3390/ijerph192214960

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates... Read More about Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.

A robust Bayesian analysis of variable selection under prior ignorance (2022)
Journal Article
Basu, T., Troffaes, M. C., & Einbeck, J. (2023). A robust Bayesian analysis of variable selection under prior ignorance. Sankhya A - Mathematical Statistics and Probability, 85(1), 1014-1057. https://doi.org/10.1007/s13171-022-00287-2

We propose a cautious Bayesian variable selection routine by investigating the sensitivity of a hierarchical model, where the regression coefficients are specified by spike and slab priors. We exploit the use of latent variables to understand the imp... Read More about A robust Bayesian analysis of variable selection under prior ignorance.

The impact of a novel medication scanner on administration errors in the hospital setting: a before and after feasibility study (2022)
Journal Article
Tolley, C. L., Watson, N. W., Heed, A., Einbeck, J., Medows, S., Wood, L., …Slight, S. P. (2022). The impact of a novel medication scanner on administration errors in the hospital setting: a before and after feasibility study. BMC Medical Informatics and Decision Making, 22(1), Article 86. https://doi.org/10.1186/s12911-022-01828-3

Objective: The medication administration process is complex and consequently prone to errors. Closed Loop Medication Administration solutions aim to improve patient safety. We assessed the impact of a novel medication scanning device (MedEye) on the... Read More about The impact of a novel medication scanner on administration errors in the hospital setting: a before and after feasibility study.

Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches (2021)
Book Chapter
Errington, A., Einbeck, J., & Cumming, J. (2021). Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches. In M. Vasile, & D. Quagliarella (Eds.), Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications (393-405). Springer Verlag. https://doi.org/10.1007/978-3-030-80542-5_24

If individuals are exposed to ionising radiation, due to some radiation accident, for medical reasons, or during spaceflight, there is often a need to estimate the contracted radiation dose. The field of biodosimetry is concerned with estimating the... Read More about Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches.

Bayesian Adaptive Selection Under Prior Ignorance (2021)
Conference Proceeding
Basu, T., Troffaes, M. C., & Einbeck, J. (2021). Bayesian Adaptive Selection Under Prior Ignorance. In M. Vasile, & D. Quagliarella (Eds.), . https://doi.org/10.1007/978-3-030-80542-5_22

Bayesian variable selection is one of the popular topics in modern day statistics. It is an important tool for high dimensional statistics, where the number of model parameters is greater than the number of observations. Several Bayesian models have... Read More about Bayesian Adaptive Selection Under Prior Ignorance.

Effects of maternal mental health on prenatal movement profiles in twins and singletons (2021)
Journal Article
Reissland, N., Einbeck, J., Wood, R., & Lane, A. (2021). Effects of maternal mental health on prenatal movement profiles in twins and singletons. Acta Paediatrica: Nurturing the Child, 110(9), 2553-2558. https://doi.org/10.1111/apa.15903

Aim: Prenatal experiences, including maternal stress, depression and anxiety, form crucial building blocks affecting the maturation of the fetal central nervous system. Previous research has examined fetal movements without considering effects of mat... Read More about Effects of maternal mental health on prenatal movement profiles in twins and singletons.

The effect of data aggregation on dispersion estimates in count data models (2021)
Journal Article
Errington, A., Einbeck, J., Cumming, J., Rössler, U., & Endesfelder, D. (2022). The effect of data aggregation on dispersion estimates in count data models. International Journal of Biostatistics, 18(1), 183-202. https://doi.org/10.1515/ijb-2020-0079

For the modelling of count data, aggregation of the raw data over certain subgroups or predictor configurations is common practice. This is, for instance, the case for count data biomarkers of radiation exposure. Under the Poisson law, count data can... Read More about The effect of data aggregation on dispersion estimates in count data models.

Multisite educational trials: estimating the effect size and its confidence intervals (2021)
Journal Article
Singh, A., Uwimpuhwe, G., Li, M., Einbeck, J., Higgins, S., & Kasim, A. (2022). Multisite educational trials: estimating the effect size and its confidence intervals. International Journal of Research & Method in Education, 45(1), 18-38. https://doi.org/10.1080/1743727x.2021.1882416

In education, multisite trials involve randomisation of pupils into intervention and comparison groups within schools. Most analytical models in multisite educational trials ignore that the impact of an intervention may be school dependent. This stud... Read More about Multisite educational trials: estimating the effect size and its confidence intervals.

Response transformations for random effect and variance component models (2020)
Journal Article
Almohaimeed, A., & Einbeck, J. (2022). Response transformations for random effect and variance component models. Statistical Modelling, 22(4), 297-326. https://doi.org/10.1177/1471082x20966919

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions... Read More about Response transformations for random effect and variance component models.

A graphical tool for assessing the suitability of a count regression model (2020)
Journal Article
Wilson, P., & Einbeck, J. (2021). A graphical tool for assessing the suitability of a count regression model. Austrian Journal of Statistics, 50(1), 1-23. https://doi.org/10.17713/ajs.v50i1.921

Whilst many numeric methods, such as AIC and deviance, exist for assessing or comparing model fit, diagrammatic methods are few. We present here a diagnostic plot, which we refer to as a `Quantile Band plot', that may be used to visually assess the s... Read More about A graphical tool for assessing the suitability of a count regression model.

Improving the accuracy of dose estimates from automatically scored dicentric chromosomes by accounting for chromosome number (2020)
Journal Article
Endesfelder, D., Kulka, U., Einbeck, J., & Oestreicher, U. (2020). Improving the accuracy of dose estimates from automatically scored dicentric chromosomes by accounting for chromosome number. International Journal of Radiation Biology, 96(12), 1571-1584. https://doi.org/10.1080/09553002.2020.1829152

Purpose: The traditional workflow for biological dosimetry based on manual scoring of dicentric chromosomes is very time consuming. Especially for large-scale scenarios or for low-dose exposures, high cell numbers have to be analysed, requiring alter... Read More about Improving the accuracy of dose estimates from automatically scored dicentric chromosomes by accounting for chromosome number.

Effects of maternal mental health on fetal visual preference for face-like compared to non-face like light stimulation (2020)
Journal Article
Reissland, N., Wood, R., Einbeck, J., & Lane, A. (2020). Effects of maternal mental health on fetal visual preference for face-like compared to non-face like light stimulation. Early Human Development, 151, Article 105227. https://doi.org/10.1016/j.earlhumdev.2020.105227

The question of whether humans react differentially to face-like versus non face-like light stimulation in the prenatal period has been much discussed, but to date has remained unresolved. In this feasibility study we have come closer to understandin... Read More about Effects of maternal mental health on fetal visual preference for face-like compared to non-face like light stimulation.

A sensitivity analysis and error bounds for the adaptive lasso (2020)
Conference Proceeding
Basu, T., Einbeck, J., & Troffaes, M. (2020). A sensitivity analysis and error bounds for the adaptive lasso. In I. Irigoien, D. -. Lee, J. Martinez-Minaya, & M. X. Rodriguez-Alvarez (Eds.), Proceedings of the 35th International Workshop on Statistical Modelling (278-281)

Sparse regression is an efficient statistical modelling technique which is of major relevance for high dimensional problems. There are several ways of achieving sparse regression, the well-known lasso being one of them. However, lasso variable select... Read More about A sensitivity analysis and error bounds for the adaptive lasso.

Binary Credal Classification Under Sparsity Constraints (2020)
Conference Proceeding
Basu, T., Troffaes, M. C., & Einbeck, J. (2020). Binary Credal Classification Under Sparsity Constraints. In M. Lesot, S. Vieira, M. Z. Reformat, J. P. Carvalho, A. Wilbik, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information processing and management of uncertainty in knowledge-based systems : 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020, proceedings, Part II (82-95). https://doi.org/10.1007/978-3-030-50143-3_7

Binary classification is a well known problem in statistics. Besides classical methods, several techniques such as the naive credal classifier (for categorical data) and imprecise logistic regression (for continuous data) have been proposed to handle... Read More about Binary Credal Classification Under Sparsity Constraints.

Prenatal effects of maternal nutritional stress and mental health on the fetal movement profile (2020)
Journal Article
Reissland, N., Millard, A., Wood, R., Ustun, B., McFaul, C., Froggatt, S., & Einbeck, J. (2020). Prenatal effects of maternal nutritional stress and mental health on the fetal movement profile. Archives of Gynecology and Obstetrics, 302(1), 65-75. https://doi.org/10.1007/s00404-020-05571-w

Purpose: Prenatal sub-optimal nutrition and exposure to maternal stress, anxiety and depression in pregnancy have been linked to increased postnatal morbidity and mortality. Fetal growth is most vulnerable to maternal dietary deficiencies, such as th... Read More about Prenatal effects of maternal nutritional stress and mental health on the fetal movement profile.

Testing fetal abilities: A commentary on studies testing prenatal reactions to light stimulation (2020)
Journal Article
Reissland, N., Wood, R., Einbeck, J., & Lane, A. Testing fetal abilities: A commentary on studies testing prenatal reactions to light stimulation. Manuscript submitted for publication

In utero conditions on fetal neurodevelopment are of current concern. One aspect relates to the question of the effects of maternal mental health and maturity of the fetus on prenatal neurodevelopment. In this feasibility study, we examined the as ye... Read More about Testing fetal abilities: A commentary on studies testing prenatal reactions to light stimulation.

A sensitivity analysis of adaptive lasso (2019)
Presentation / Conference
Basu, T., Einbeck, J., & Troffaes, M. C. (2019, December). A sensitivity analysis of adaptive lasso. Paper presented at Innovations in Data and Statistical Sciences (INDSTATS 2019), Mumbai, India

Practical Considerations on Nonparametric Methods for Estimating Intrinsic Dimensions of Nonlinear Data Structures (2019)
Journal Article
Einbeck, J., Kalantan, Z., & Kruger, U. (2020). Practical Considerations on Nonparametric Methods for Estimating Intrinsic Dimensions of Nonlinear Data Structures. International Journal of Pattern Recognition and Artificial Intelligence, 34(9), Article 2058010. https://doi.org/10.1142/s0218001420580100

This paper develops readily applicable methods for estimating the intrinsic dimension of multivariate datasets. The proposed methods, which make use of theoretical properties of the empirical distribution functions of (pairwise or pointwise) distance... Read More about Practical Considerations on Nonparametric Methods for Estimating Intrinsic Dimensions of Nonlinear Data Structures.

Quantile-Based Estimation of the Finite Cauchy Mixture Model (2019)
Journal Article
Kalantan, Z. I., & Einbeck, J. (2019). Quantile-Based Estimation of the Finite Cauchy Mixture Model. Symmetry, 11(9), Article 1186. https://doi.org/10.3390/sym11091186

Heterogeneity and outliers are two aspects which add considerable complexity to the analysis of data. The Cauchy mixture model is an attractive device to deal with both issues simultaneously. This paper develops an Expectation-Maximization-type algor... Read More about Quantile-Based Estimation of the Finite Cauchy Mixture Model.

A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay (2018)
Journal Article
Einbeck, J., Ainsbury, E. A., Sales, R., Barnard, S., Kaestle, F., & Higueras, M. (2018). A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay. PLoS ONE, 13(11), https://doi.org/10.1371/journal.pone.0207464

Over the last decade, the γ–H2AX focus assay, which exploits the phosphorylation of the H2AX histone following DNA double–strand–breaks, has made considerable progress towards acceptance as a reliable biomarker for exposure to ionizing radiation. Whi... Read More about A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay.

Box-Cox response transformations for random effect models (2018)
Conference Proceeding
Almohaimeed, A., & Einbeck, J. (2018). Box-Cox response transformations for random effect models. In Proceedings of the 33rd International Workshop on Statistical Modelling (1-6)

For the linear model with random effects of unspecified distribution, we develop methodology for simultaneous response transformation and estimation of regres- sion parameters. This is achieved by extending the Nonparametric Maximum Likelihood toward... Read More about Box-Cox response transformations for random effect models.

A new and intuitive test for zero modification (2018)
Journal Article
Wilson, P., & Einbeck, J. (2019). A new and intuitive test for zero modification. Statistical Modelling, 19(4), 341--361. https://doi.org/10.1177/1471082x18762277

While there do exist several statistical tests for detecting zero modification in count data regression models, these rely on asymptotical results and do not transparently distinguish between zero inflation and zero deflation. In this manuscript, a n... Read More about A new and intuitive test for zero modification.

Fisher information under Gaussian quadrature models (2017)
Journal Article
Marques da Silva Júnior, A. H., Einbeck, J., & Craig, P. S. (2018). Fisher information under Gaussian quadrature models. Statistica Neerlandica, 72(2), 74-89. https://doi.org/10.1111/stan.12116

This paper develops formulae to compute the Fisher information matrix for the regression parameters of generalized linear models with Gaussian random effects. The Fisher information matrix relies on the estimation of the response variance under the m... Read More about Fisher information under Gaussian quadrature models.

Sample quantiles corresponding to mid p-values for zero-modification tests (2017)
Conference Proceeding
Einbeck, J., & Wilson, P. (2017). Sample quantiles corresponding to mid p-values for zero-modification tests. In M. Grzegorczyk, & G. Ceoldo (Eds.), Proceedings of the 32nd International Workshop on Statistical Modelling : Groningen, Netherlands, 3-7 July, 2017 (275-279)

On the Use of Random Effect Models for Radiation Biodosimetry (2017)
Book Chapter
Einbeck, J., Ainsbury, E., Barnard, S., Oliveira, M., Manning, G., Puig, P., & Badie, C. (2017). On the Use of Random Effect Models for Radiation Biodosimetry. In E. Ainsbury, M. Calle, E. Cardis, J. Einbeck, G. Gómez, & P. Puig (Eds.), Extended abstracts Fall 2015 : Biomedical Big Data ; Statistics for Low Dose Radiation Research (89-94). Springer Verlag. https://doi.org/10.1007/978-3-319-55639-0_15

The application of random effect models to different radiation biomarkers, including cytogenetic, protein-based, and gene-expression based biomarkers, is discussed. Explicit case studies are provided for the latter two scenarios, in which random effe... Read More about On the Use of Random Effect Models for Radiation Biodosimetry.

Self–consistency–based tests for bivariate distributions (2017)
Journal Article
Einbeck, J., & Meintanis, S. (2017). Self–consistency–based tests for bivariate distributions. Journal of statistical theory and practice, 11(3), 478-492. https://doi.org/10.1080/15598608.2017.1318098

A novel family of tests based on the self–consistency property is developed. Our developments can be motivated by the well known fact that a two–dimensional spherically symmetric distribution X is self–consistent w.r.t. to the circle E||X||, that is... Read More about Self–consistency–based tests for bivariate distributions.

Mixture Models for Prediction from Time Series, with Application to Energy Use Data (2017)
Journal Article
Qarmalah, N. M., Einbeck, J., & Coolen, F. P. (2017). Mixture Models for Prediction from Time Series, with Application to Energy Use Data. Archives of data science. Series A, 2(1), 1-15. https://doi.org/10.5445/ksp/1000058749/07

This paper aims to use mixture models to produce predictions from time series data. Given data of the form (ti, yi), i = 1, . . . , T , we propose a mix- ture model localized at time point tT with the k-th component as yi = mk (ti) + εik with mixing... Read More about Mixture Models for Prediction from Time Series, with Application to Energy Use Data.

Con fidence intervals for posterior intercepts, with application to the PIAAC literacy survey (2017)
Conference Proceeding
Einbeck, J., Gray, E., Sofroniou, N., Marques da Silva Junior, A., & Gledhill, J. (2017). Con fidence intervals for posterior intercepts, with application to the PIAAC literacy survey. In M. Grzegorczyk, & G. Ceoldo (Eds.), Proceedings of the 32nd International Workshop on Statistical Modelling : Groningen, Netherlands, 3-7 July, 2017 (217-222)

For variance component models, it is often the posterior estimate of the random effect (‘posterior intercept’) rather than the estimate of the fixed effect parameters, which is of main interest. This is the case, for instance, when ranking region–wis... Read More about Con fidence intervals for posterior intercepts, with application to the PIAAC literacy survey.

The correlation threshold as a strategy for gene filtering, with application to irritable bowel syndrome and breast cancer microarray data (2016)
Journal Article
Jackson, S. E., Einbeck, J., Kasim, A., & Talloen, W. (2016). The correlation threshold as a strategy for gene filtering, with application to irritable bowel syndrome and breast cancer microarray data. Reinvention, 9(2),

It is well established in the literature that certain disease-associated gene signatures can be identified as a source for predicting the classification of samples or cell lines into diagnostic groups – for example, healthy and diseased. Using standa... Read More about The correlation threshold as a strategy for gene filtering, with application to irritable bowel syndrome and breast cancer microarray data.

Uncertainty of fast biological radiation dose assessment for emergency response scenarios (2016)
Journal Article
Ainsbury, E. A., Higueras, M., Puig, P., Einbeck, J., Samaga, D., Barquinero, J. F., …Woda, C. (2017). Uncertainty of fast biological radiation dose assessment for emergency response scenarios. International Journal of Radiation Biology, 93(1), 127-135. https://doi.org/10.1080/09553002.2016.1227106

Purpose: Reliable dose estimation is an important factor in appropriate dosimetric triage categorization of exposed individuals to support radiation emergency response. Materials and Methods: Following work done under the EU FP7 MULTIBIODOSE and RENE... Read More about Uncertainty of fast biological radiation dose assessment for emergency response scenarios.

A diagnostic plot for assessing model fit in count data models (2016)
Conference Proceeding
Einbeck, J., & Wilson, P. (2016). A diagnostic plot for assessing model fit in count data models. In J. F. Dupuy, & J. Josse (Eds.), Proceedings of the 31st International Workshop on Statistical Modelling. July 4-8, 2016, Rennes, France (103-108)

Whilst many numeric methods, such as AIC and deviance, exist for assessing model fit, diagrammatic methods are few. We present here a diagnostic plot, to which we refer as `Christmas tree plot' due its characteristic shape, that may be used to visual... Read More about A diagnostic plot for assessing model fit in count data models.

Gradient test for generalised linear models with random effects (2016)
Conference Proceeding
da Silva-Junior, A., Einbeck, J., & Craig, P. (2016). Gradient test for generalised linear models with random effects. In J. F. Dupuy, & J. Josse (Eds.), Proceedings of the 31st International Workshop on Statistical Modelling. July 4-8, 2016, Rennes, France (213-218)

This work develops the gradient test for parameter selection in generalised linear models with random effects. Asymptotically, the test statistic has a chi-squared distribution and the statistic has a compelling feature: it does not require computati... Read More about Gradient test for generalised linear models with random effects.

k-Boxplots for mixture data (2016)
Journal Article
Qarmalah, N. M., Einbeck, J., & Coolen, F. P. (2018). k-Boxplots for mixture data. Statistical Papers, 59(2), 513-528. https://doi.org/10.1007/s00362-016-0774-7

This article introduces a new graphical tool to summarize data which possess a mixture structure. Computation of the required summary statistics makes use of posterior probabilities of class membership which can be obtained from a fitted mixture mode... Read More about k-Boxplots for mixture data.

On statistical testing and mean parameter estimation for zero-modification in count data regression (2016)
Conference Proceeding
Wilson, P., & Einbeck, J. (2016). On statistical testing and mean parameter estimation for zero-modification in count data regression. In J. F. Dupuy, & J. Josse (Eds.), Proceedings of the 31st International Workshop on Statistical Modelling. July 4-8, 2016, Rennes, France (327-332)

For the problem of testing for zero-modification in Poisson regression, a simple and intuitive test can be constructed by computing directly confidence intervals for the number of 0's under the Poisson assumption. This requires the ability of estimat... Read More about On statistical testing and mean parameter estimation for zero-modification in count data regression.

Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study (2015)
Journal Article
Oliveira, M., Einbeck, J., Higueras, M., Ainsbury, E., Puig, P., & Rothkamm, K. (2016). Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study. Biometrical Journal, 58(2), 259-279. https://doi.org/10.1002/bimj.201400233

Within the field of cytogenetic biodosimetry, Poisson regression is the classical approach for modeling the number of chromosome aberrations as a function of radiation dose. However, it is common to find data that exhibit overdispersion. In practice,... Read More about Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study.

Hotspots in Hindsight (2015)
Book Chapter
Julian, B., Foulger, G., Hatfield, O., Jackson, S., Simpson, E., Einbeck, J., & Moore, A. (2015). Hotspots in Hindsight. In G. Foulger, M. Lustrino, & S. King (Eds.), The Interdisciplinary Earth: A Volume in Honor of Don L. Anderson (105-121). The Geological Society of America / AGU. https://doi.org/10.1130/2015.2514%2808%29

Several workers have suggested that the locations of melting anomalies (hotspots) and the original locations of large igneous provinces (LIPs) and kimberlite pipes lie preferentially above the margins of two large low-shear-velocity provinces, or LLS... Read More about Hotspots in Hindsight.

A summer with genes: Simple disease classification from microarray data (2015)
Journal Article
Einbeck, J., Jackson, S. E., & Kasim, A. (2015). A summer with genes: Simple disease classification from microarray data. Mathematics today, 51(4), 186-188

In this article we report on the work carried out within the framework of a summer project, part-funded by an IMA small grant, in which an undergraduate student (the second author of this manuscript) developed and implemented methodology for disease... Read More about A summer with genes: Simple disease classification from microarray data.

A simple and intuitive test for number-inflation or number-deflation (2015)
Conference Proceeding
Wilson, P., & Einbeck, J. (2015). A simple and intuitive test for number-inflation or number-deflation. In H. Wagner, & H. Friedl (Eds.), Proceedings of the 30th International Workshop on Statistical Modelling. Linz, Austria, 6-10 July 2015 (299-302)

We present a test of zero-modification which checks if the number of zeros is consistent with the hypothesized count distribution. This test is easily extended to test for inflation or deflation of any non-negative values, and, by performing multiple... Read More about A simple and intuitive test for number-inflation or number-deflation.

Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction (2014)
Conference Proceeding
Bonetti, D., Delbem, A., & Einbeck, J. (2014). Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction. In T. Kneib, F. Sobotka, J. Fahrenholz, & H. Irmer (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (15-18)

A real-valued bivariate ‘Estimation of Distribution Algorithm’ specific for the ab initio and full-atom Protein Structure Prediction problem is proposed. It is known that this is a multidimensional and multimodal problem. In order to deal with the mu... Read More about Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction.

Bayesian shape modelling of cross-sectional geological data (2014)
Conference Proceeding
Tsiftsi, T., Jermyn, I., & Einbeck, J. (2014). Bayesian shape modelling of cross-sectional geological data. In K. Thomas, S. Fabian, F. Jan, & I. Henriette (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (161-164)

Shape information is of great importance in many applications. For example, the oil-bearing capacity of sand bodies, the subterranean remnants of ancient rivers, is related to their cross-sectional shapes. The analysis of these shapes is therefore of... Read More about Bayesian shape modelling of cross-sectional geological data.

A study of online and blockwise updating of the EM algorithm for Gaussian mixtures (2014)
Conference Proceeding
Einbeck, J., & Bonetti, D. (2014). A study of online and blockwise updating of the EM algorithm for Gaussian mixtures. In T. Kneib, F. Sobotka, J. Fahrenholz, & H. Irmer (Eds.), 29th International Workshop on Statistical Modelling, 14-18 July 2014, Göttingen, Germany ; proceedings (35-38)

A variant of the EM algorithm for the estimation of multivariate Gaussian mixtures, which allows for online as well as blockwise updating of sequentially obtained parameter estimates, is investigated. Several dierent update schemes are considered and... Read More about A study of online and blockwise updating of the EM algorithm for Gaussian mixtures.

Implementation of a local principal curves algorithm for neutrino interaction reconstruction in a liquid argon volume (2014)
Journal Article
Back, J., Barker, G., Boyd, S., Einbeck, J., Haigh, M., Morgan, B., …Roythorne, D. (2014). Implementation of a local principal curves algorithm for neutrino interaction reconstruction in a liquid argon volume. The European Physical Journal C, 74(3), Article 2832. https://doi.org/10.1140/epjc/s10052-014-2832-4

A local principal curve algorithm has been implemented in three dimensions for automated track and shower reconstruction of neutrino interactions in a liquid argon time projection chamber. We present details of the algorithm and characterise its perf... Read More about Implementation of a local principal curves algorithm for neutrino interaction reconstruction in a liquid argon volume.

Some asymptotics for localized principal components and curves (2013)
Journal Article
Einbeck, J., & Zayed, M. (2014). Some asymptotics for localized principal components and curves. Communications in Statistics - Theory and Methods, 43(8), 1736-1749. https://doi.org/10.1080/03610926.2012.673676

The asymptotic behavior of localized principal components applying kernels as weights is investigated. In particular, we show that the first-order approximation of the first localized principal component at any given point only depends on the bandwid... Read More about Some asymptotics for localized principal components and curves.

Validation tests for semi-parametric models (2013)
Journal Article
Meintanis, S., & Einbeck, J. (2013). Validation tests for semi-parametric models. Journal of Statistical Computation and Simulation, 85(1), 131-146. https://doi.org/10.1080/00949655.2013.806922

Tests are proposed for validation of the hypothesis that a partial linear regression model adequately describes the structure of a given data set. The test statistics are formulated following the approach of Fourier-type conditional expectations firs... Read More about Validation tests for semi-parametric models.

Challenging the curse of dimensionality in multivariate local linear regression (2013)
Journal Article
Taylor, J., & Einbeck, J. (2013). Challenging the curse of dimensionality in multivariate local linear regression. Computational Statistics, 28(3), 955-976. https://doi.org/10.1007/s00180-012-0342-0

Local polynomial fitting for univariate data has been widely studied and discussed, but up until now the multivariate equivalent has often been deemed impractical, due to the so-called curse of dimensionality. Here, rather than discounting it complet... Read More about Challenging the curse of dimensionality in multivariate local linear regression.

A number-of-modes reference rule for density estimation under multimodality (2013)
Journal Article
Einbeck, J., & Taylor, J. (2013). A number-of-modes reference rule for density estimation under multimodality. Statistica Neerlandica, 67(1), 54-66. https://doi.org/10.1111/j.1467-9574.2012.00531.x

We consider kernel density estimation for univariate distributions. The question of interest is as follows: given that the data analyst has some background knowledge on the modality of the data (for instance, ‘data of this type are usually bimodal’),... Read More about A number-of-modes reference rule for density estimation under multimodality.

Goodness-of-fit tests in semi-linear models (2012)
Journal Article
Meintanis, S., & Einbeck, J. (2012). Goodness-of-fit tests in semi-linear models. Statistics and Computing, 22(4), 967-979. https://doi.org/10.1007/s11222-011-9266-8

Specification tests for the error distribution are proposed in semi-linear models, including the partial linear model and additive models. The tests utilize an integrated distance involving the empirical characteristic function of properly estimated... Read More about Goodness-of-fit tests in semi-linear models.

Penalized regression on principal manifolds with application to combustion modelling (2012)
Conference Proceeding
Einbeck, J., Isaac, B., Evers, L., & Parente, A. (2012). Penalized regression on principal manifolds with application to combustion modelling. In A. Komarek, & S. Nagy (Eds.), 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings (117-122)

For multivariate regression problems featuring strong and non–linear dependency patterns between the involved predictors, it is attractive to reduce the dimension of the estimation problem by approximating the predictor space through a principal surf... Read More about Penalized regression on principal manifolds with application to combustion modelling.

Generative linear mixture modelling (2012)
Conference Proceeding
Lawson, A., & Einbeck, J. (2012). Generative linear mixture modelling. In A. Komarek, & S. Nagy (Eds.), 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings (595-600)

For multivariate data with a low–dimensional latent structure, a novel approach to linear dimension reduction based on Gaussian mixture models is pro- posed. A generative model is assumed for the data, where the mixture centres (or ‘mass points’) are... Read More about Generative linear mixture modelling.

Multivariate regression smoothing through the 'fallling net' (2011)
Conference Proceeding
Taylor, J., & Einbeck, J. (2011). Multivariate regression smoothing through the 'fallling net'. In D. Conesa, A. Forte, A. Lopez-Quilez, & F. Munoz (Eds.), 26th International Workshop on Statistical Modelling, 5-11 July 2011, Valencia, Spain ; proceedings (597-602)

We consider multivariate regression smoothing through a conditional mean shift procedure. By computing local conditional means iteratively over a set or grid of target points, at each iteration a `net' is formed which gently drifts towards the data c... Read More about Multivariate regression smoothing through the 'fallling net'.

Using principal curves to analyse traffic patterns on freeways (2011)
Journal Article
Einbeck, J., & Dwyer, J. (2011). Using principal curves to analyse traffic patterns on freeways. Transportmetrica, 7(3), 229-246. https://doi.org/10.1080/18128600903500110

Scatterplots of traffic speed versus flow have caught considerable attention over the last decades due to their characteristic half-moon like shape. Modelling data of this type is difficult as both variables are actually not a function of each other... Read More about Using principal curves to analyse traffic patterns on freeways.

Bandwidth Selection for Mean-shift based Unsupervised Learning Techniques: a Unified Approach via Self-coverage (2011)
Journal Article
Einbeck, J. (2011). Bandwidth Selection for Mean-shift based Unsupervised Learning Techniques: a Unified Approach via Self-coverage. Journal of pattern recognition research, 6(2), 175-192. https://doi.org/10.13176/11.288

The mean shift is a simple but powerful tool emerging from the computer science literature which shifts a point to the local center of mass around this point. It has been used as a building block for several nonparametric unsupervised learning techni... Read More about Bandwidth Selection for Mean-shift based Unsupervised Learning Techniques: a Unified Approach via Self-coverage.

Constructing Economic Summary Indexes via Principal Curves (2010)
Conference Proceeding
Zayed, M., & Einbeck, J. (2010). Constructing Economic Summary Indexes via Principal Curves. In Y. Lechevallier, & G. Saporta (Eds.),

Index number construction is an important and traditional subject in both the statistical and the economical sciences. A novel technique based on localized principal components to compose a single summary index from a collection of indexes is propose... Read More about Constructing Economic Summary Indexes via Principal Curves.

Data compression and regression through local principal curves and surfaces (2010)
Journal Article
Einbeck, J., Evers, L., & Powell, B. (2010). Data compression and regression through local principal curves and surfaces. International Journal of Neural Systems, 20(3), 177-192. https://doi.org/10.1142/s0129065710002346

We consider principal curves and surfaces in the context of multivariate regression modelling. For predictor spaces featuring complex dependency patterns between the involved variables, the intrinsic dimensionality of the data tends to be very small... Read More about Data compression and regression through local principal curves and surfaces.

Data compression and regression based on local principal curves (2010)
Conference Proceeding
Einbeck, J., Evers, L., & Hinchliff, K. (2010). Data compression and regression based on local principal curves. In A. Fink, B. Lausen, W. Seidel, & A. Ultsch (Eds.), Advances in data analysis, data handling and business intelligence (701-712). https://doi.org/10.1007/978-3-642-01044-6_64

Frequently the predictor space of a multivariate regression problem of the type y = m(x_1, …, x_p ) + ε is intrinsically one-dimensional, or at least of far lower dimension than p. Usual modeling attempts such as the additive model y = m_1(x_1) + … +... Read More about Data compression and regression based on local principal curves.

Representing complex data using localized principal components with application to astronomical data. (2008)
Book Chapter
Einbeck, J., Evers, L., & Bailer-Jones, C. (2008). Representing complex data using localized principal components with application to astronomical data. In A. Gorban, B. Kegl, D. Wunsch, & A. Zinovyev (Eds.), Lecture Notes in Computational Science and Engineering (180-204). Springer Verlag. https://doi.org/10.1007/978-3-540-73750-6_7

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex'... Read More about Representing complex data using localized principal components with application to astronomical data..

League tables for literacy survey data based on random effect models (2008)
Conference Proceeding
Sofroniou, N., Hoad, D., & Einbeck, J. (2008). League tables for literacy survey data based on random effect models. In P. Eilers (Ed.), 23rd International Workshop on Statistical Modelling, 7-11 July 2008, Utrecht ; proceedings (402-405)

Data from the International Adult Literacy Survey are used to illustrate how league tables can be obtained from summary data, consisting of percentages and their standard errors, using random effects models estimated by nonparametric maximum likeliho... Read More about League tables for literacy survey data based on random effect models.

Weighted Repeated Median Smoothing and Filtering (2007)
Journal Article
Fried, R., Einbeck, J., & Gather, U. (2007). Weighted Repeated Median Smoothing and Filtering. Journal of the American Statistical Association, 102(480), 1300-1308. https://doi.org/10.1198/016214507000001166

We propose weighted repeated median filters and smoothers for robust non-parametric regression in general and for robust online signal extraction from time series in particular. The new methods allow to remove outlying sequences and to preserve disco... Read More about Weighted Repeated Median Smoothing and Filtering.

A comparative study of nonparametric derivative estimators. (2007)
Conference Proceeding
Newell, J., & Einbeck, J. (2007). A comparative study of nonparametric derivative estimators. In J. del Castillo, A. Espinal, & P. Puig (Eds.),

Nonparametric derivative estimation has never attracted much attention as one gets the derivative estimates as ``by-products'' from a local polynomial or spline fit. However, these estimates often suffer from boundary effects and are very sensitive t... Read More about A comparative study of nonparametric derivative estimators..

Smoothing, Sampling, and Basu's elephants (2007)
Conference Proceeding
Einbeck, J., Augustin, T., & Singer, J. M. (2007). Smoothing, Sampling, and Basu's elephants. In J. del Castillo, A. Espinal, & P. Puig (Eds.),

We investigate design-weighted local smoothing and show that the optimal (bias-minimizing) weights have similar form and interpretation as the optimal weights given by the Horvitz-Thompson theorem known from sampling theory. We set forth that the haz... Read More about Smoothing, Sampling, and Basu's elephants.

A new package for fitting random effect models (2007)
Journal Article
Einbeck, J., Hinde, J., & Darnell, R. (2007). A new package for fitting random effect models. R news, 7(1), 26-30

Random effects have become a standard concept in statistical modelling over the last decades. They enter a wide range of applications by providing a simple tool to account for such problems as model misspecification, unobserved (latent) variables, un... Read More about A new package for fitting random effect models.

On design-weighted local fitting and its relation to the Horvitz-Thompson estimator (2007)
Journal Article
Einbeck, J., & Augustin, T. (2007). On design-weighted local fitting and its relation to the Horvitz-Thompson estimator. Statistica sinica, 19(1), 103-123

Weighting is a widely used concept in many fields of statistics and has frequently caused controversies on its justification and benefit. In this paper, we analyze design-weighted versions of the well-known local polynomial regression estimators, der... Read More about On design-weighted local fitting and its relation to the Horvitz-Thompson estimator.

Modelling beyond regression functions: An application of multimodal regression to speed-flow data (2006)
Journal Article
Einbeck, J., & Tutz, G. (2006). Modelling beyond regression functions: An application of multimodal regression to speed-flow data. Journal of the Royal Statistical Society: Series C, 55(4), 461-475. https://doi.org/10.1111/j.1467-9876.2006.00547.x

For speed–flow data, which are intensively discussed in transportation science, common nonparametric regression models of the type y=m(x)+noise turn out to be inadequate since simple functional models cannot capture the essential relationship between... Read More about Modelling beyond regression functions: An application of multimodal regression to speed-flow data.

The fitting of multifunctions: an approach to nonparametric multimodal regression (2006)
Conference Proceeding
Einbeck, J., & Tutz, G. (2006). The fitting of multifunctions: an approach to nonparametric multimodal regression. In A. Rizzi, & M. Vichi (Eds.), COMPSTAT 2006 : proceedings in computational statistics, 17th symposium held in Rome, Italy, 2006 (1251-1258)

In the last decades a lot of research has been devoted to smoothing in the sense of nonparametric regression. However, this work has nearly exclusively concentrated on fitting regression functions. When the conditional distribution of y|x is multimod... Read More about The fitting of multifunctions: an approach to nonparametric multimodal regression.

Analyzing Irish suicide rates with mixture models (2006)
Conference Proceeding
Sofroniou, N., Einbeck, J., Hinde, J., & Newell, J. (2006). Analyzing Irish suicide rates with mixture models. In Proceedings of the 21st International Workshop on Statistical Modelling: IWSM 2006, 3-7 July 2006, Galway, Ireland (474-481)

In the analysis of morbidity and mortality data, variance component models are commonly used to provide an improvement in the estimation of rates for small regions which typically show large variability. This article investigates Irish suicide data u... Read More about Analyzing Irish suicide rates with mixture models.

A note on NPML estimation for exponential family regression models with unspecified dispersion parameter (2006)
Journal Article
Einbeck, J., & Hinde, J. (2006). A note on NPML estimation for exponential family regression models with unspecified dispersion parameter. Austrian Journal of Statistics, 35(2&3), 233-243

Nonparametric maximum likelihood (NPML) estimation for exponential families with unspecified dispersion parameter \phi suffers from computational instability, which can lead to highly fluctuating EM trajectories and suboptimal solutions, in particula... Read More about A note on NPML estimation for exponential family regression models with unspecified dispersion parameter.

Model free endurance markers based on the second derivative of blood lactate curves (2005)
Conference Proceeding
Newell, J., Einbeck, J., Madden, N., & McMillan, K. (2005). Model free endurance markers based on the second derivative of blood lactate curves. In A. R. Francis, K. M. Matawie, A. Oshlack, & G. K. Smyth (Eds.), Statistical solutions to modern problems ; proceedings of the 20th International Workshop on Statistical Modelling. Sydney, Australia, July 10-15, 2005 (357-364)

Blood lactate assessment is used regularly by exercise physiologists as a predictor of endurance performance. Typically fingertip blood samples are collected at selected running speeds on a treadmill and a plot of lactate concentration against worklo... Read More about Model free endurance markers based on the second derivative of blood lactate curves.

Exploring Multivariate Data Structures with Local Principal Curves (2005)
Conference Proceeding
Einbeck, J., Tutz, G., & Evers, L. (2005). Exploring Multivariate Data Structures with Local Principal Curves. In C. Weihs, & W. Gaul (Eds.), Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation, 9-11 March 2004, University of Dortmund (256-263)

A new approach to find the underlying structure of a multidimensional data cloud is proposed, which is based on a localized version of principal components analysis. More specifically, we calculate a series of local centers of mass and move through t... Read More about Exploring Multivariate Data Structures with Local Principal Curves.

Local fitting with a power basis (2004)
Journal Article
Einbeck, J. (2004). Local fitting with a power basis. Revstat Statistical Journal, 2(2), 102-126

Local polynomial modelling can be seen as a local fit of the data against a polynomial basis. In this paper we extend this method to the power basis, i.e. a basis which consists of the powers of an arbitrary function. Using an extended Taylor theorem... Read More about Local fitting with a power basis.

Local Smoothing with Robustness against outlying Predictors (2004)
Journal Article
Einbeck, J., Andre, C. D., & Singer, J. M. (2004). Local Smoothing with Robustness against outlying Predictors. Environmetrics, 15(6), 541-554. https://doi.org/10.1002/env.644

Outlying pollutant concentration data are frequently observed in time series studies conducted to investigate the effects of atmospheric pollution on mortality/morbidity. These outliers may severely affect the estimation procedures and even generate... Read More about Local Smoothing with Robustness against outlying Predictors.

A Simple Unifying Formula for Taylor's Theorem and Cauchy's Mean Value Theorem (2004)
Journal Article
Einbeck, J. (2004). A Simple Unifying Formula for Taylor's Theorem and Cauchy's Mean Value Theorem. International Journal of Pure and Applied Mathematics, 14(1), 69-74

We introduce a formula which generalizes Taylor's theorem from powers of linear terms z-x to functional terms \phi(z)-\phi(x), leading to a formula which reduces in a special case to Cauchy's generalized mean value theorem. In other words, regarding... Read More about A Simple Unifying Formula for Taylor's Theorem and Cauchy's Mean Value Theorem.

Online Monitoring with Local Smoothing Methods and Adaptive Ridging (2003)
Journal Article
Einbeck, J., & Kauermann, G. (2003). Online Monitoring with Local Smoothing Methods and Adaptive Ridging. Journal of Statistical Computation and Simulation, 73(12), 913-929. https://doi.org/10.1080/0094965031000104332

We consider online monitoring of sequentially arising data as e.g. met in clinical information systems. The general focus thereby is to detect breakpoints, i.e. timepoints where the measurement series suddenly changes the general level. The method su... Read More about Online Monitoring with Local Smoothing Methods and Adaptive Ridging.

Multivariate Local Fitting with General Basis Functions (2003)
Journal Article
Einbeck, J. (2003). Multivariate Local Fitting with General Basis Functions. Computational Statistics, 18(2), 185-203. https://doi.org/10.1007/s001800300140

In this paper we combine the concepts of local smoothing and fitting with basis functions for multivariate predictor variables. We start with arbitrary basis functions and show that the asymptotic variance at interior points is independent of the cho... Read More about Multivariate Local Fitting with General Basis Functions.