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Wednesday, June 29 • 2:12pm - 2:30pm
brglm: Reduced-bias inference in generalized linear models

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This presentation focuses on the brglm R package, which provides methods for reduced-bias inference in univariate generalised linear models and multinomial regression models with either ordinal or nominal responses (Kosmidis, 2014, JRSSB and Kosmidis and Firth, 2011, Biometrika, respectively). The core fitting method is based on the iterative correction of the bias of the maximum likelihood estimator, and results in the solution of appropriate bias-reducing adjusted score equations. For multinomial logistic regression, we present alternative algorithms that can scale up well with the number of multinomial responses and illustrate the finiteness and shrinkage properties that make bias reduction attractive for such models. For families with dispersion parameters (e.g. gamma regression), brglm uses automatic differentiation to compute the reduced-bias estimator of arbitrary invertible transformations of the dispersion parameter (e.g. user-supplied). We also present the implementation of appropriate methods for inference when bias-reduced estimation is being used.

avatar for Patrícia Martinková

Patrícia Martinková

Researcher, Institute of Computer Science, Czech Academy of Sciences
Researcher in statistics and psychometrics from Prague. Uses R to boost active learning in classes. Fulbright alumna and 2013-2015 visiting research scholar with Center for Statistics and the Social Sciences and Department of Statistics, University of Washington.

avatar for Ioannis  Kosmidis

Ioannis Kosmidis

Associate Professor, Department of Statistical Science, University College London
I am a Senior Lecturer at the Department of Statistical Science in University College London. My theoretical and methodological research focuses on optimal estimation and inference from complex statistical models, penalized likelihood methods and clustering. A particular focus of... Read More →

Wednesday June 29, 2016 2:12pm - 2:30pm PDT
Econ 140