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07/14/2023
Bounded-memory adjusted scores estimation in generalized linear models with large data sets
The widespread use of maximum Jeffreys'-prior penalized likelihood in bi...
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06/06/2022
Maximum softly-penalized likelihood for mixed effects logistic regression
Maximum likelihood estimation in logistic regression with mixed effects ...
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12/05/2021
Mean and median bias reduction: A concise review and application to adjacent-categories logit models
The estimation of categorical response models using bias-reducing adjust...
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05/30/2021
Scalable and Interpretable Marked Point Processes
We introduce a novel inferential framework for marked point processes th...
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03/08/2021
Flexible marked spatio-temporal point processes with applications to event sequences from association football
We develop a new family of marked point processes by focusing the charac...
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01/18/2021
Bias Reduction as a Remedy to the Consequences of Infinite Estimates in Poisson and Tobit Regression
Data separation is a well-studied phenomenon that can cause problems in ...
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10/30/2020
Parametric bootstrap inference for stratified models with high-dimensional nuisance specifications
Inference about a scalar parameter of interest typically relies on the a...
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01/11/2020
Empirical bias-reducing adjustments to estimating functions
We develop a novel, general framework for the asymptotic reduction of th...
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09/16/2019
Davidson-Luce model for multi-item choice with ties
This paper introduces a natural extension of the pair-comparison-with-ti...
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12/05/2018
Jeffreys' prior, finiteness and shrinkage in binomial-response generalized linear models
This paper studies the finiteness properties of a reduced-bias estimator...
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10/29/2018
Modelling rankings in R: the PlackettLuce package
This paper presents the R package PlackettLuce, which implements a gener...
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07/04/2018
Modeling outcomes of soccer matches
We compare various extensions of the Bradley-Terry model and a hierarchi...
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04/11/2018
Mean and median bias reduction in generalized linear models
This paper presents an integrated framework for estimation and inference...
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02/22/2018
Sparse Bayesian dynamic network models, with genomics applications
Network models have become an important topic in modern statistics, and ...
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01/26/2018
