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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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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.

A sensitivity analysis of adaptive lasso (2019)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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.