Outputs (9)

Robust Bayesian causal estimation for causal inference in medical diagnosis (2024)
Journal Article
Basu, T., & Troffaes, M. C. M. (2025). Robust Bayesian causal estimation for causal inference in medical diagnosis. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 177, Article 109330. https://doi.org/10.1016/j.ijar.2024.109330

Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment... Read More about Robust Bayesian causal estimation for causal inference in medical diagnosis.

A Robust Bayesian Approach for Causal Inference Problems (2023)
Presentation / Conference Contribution
Basu, T., Troffaes, M. C. M., & Einbeck, J. (2023, September). A Robust Bayesian Approach for Causal Inference Problems. Presented at ECSQARU 2023, Arras, France

Causal inference concerns finding the treatment effect on subjects along with causal links between the variables and the outcome. However, the underlying heterogeneity between subjects makes the problem practically unsolvable. Additionally, we often... Read More about A Robust Bayesian Approach for Causal Inference Problems.

Bayesian Adaptive Selection Under Prior Ignorance (2021)
Presentation / Conference Contribution
Basu, T., Troffaes, M. C., & Einbeck, J. (2021, December). Bayesian Adaptive Selection Under Prior Ignorance. Presented at UQOP 2020

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.

Uncertainty Quantification in Lasso-Type Regularization Problems (2020)
Book Chapter
Basu, T., Einbeck, J., & Troffaes, M. C. (2021). Uncertainty Quantification in Lasso-Type Regularization Problems. In Optimization Under Uncertainty with Applications to Aerospace Engineering (81-109). Springer Verlag. https://doi.org/10.1007/978-3-030-60166-9_3

A sensitivity analysis and error bounds for the adaptive lasso (2020)
Presentation / Conference Contribution
Basu, T., Einbeck, J., & Troffaes, M. (2020, December). A sensitivity analysis and error bounds for the adaptive lasso. Presented at International Workshop on Statistical Modelling, Bilbao

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, December). Binary Credal Classification Under Sparsity Constraints. Presented at Information Processing and Management of Uncertainty in Knowledge-Based Systems, Lisbon

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.

A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability (2019)
Presentation / Conference Contribution
Troffaes, M. C., & Basu, T. (2019, December). A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability. Presented at ISIPTA'19, Ghent

In this paper we prove a new probability inequality that can be used to construct p-boxes in a non-parametric fashion, using the sample mean and sample standard deviation instead of the true mean and true standard deviation. The inequality relies onl... Read More about A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability.

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

Robust uncertainty quantification for measurement problems with limited information (2019)
Presentation / Conference Contribution
Basu, T., Einbeck, J., Troffaes, M. C., & Forbes, A. (2019, July). Robust uncertainty quantification for measurement problems with limited information. Paper presented at ISIPTA 2019, Ghent, Belgium