Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Kamil Fijorek, Andrzej Sokolowski
Title: Separation-Resistant and Bias-Reduced Logistic Regression: STATISTICA Macro
Abstract: Logistic regression is one of the most popular techniques used to describe the relationship between a binary dependent variable and a set of independent variables. However, the application of logistic regression to small data sets is often hindered by the complete or quasicomplete separation. Under the separation scenario, results obtained via maximum likelihood should not be trusted, since at least one parameter estimate diverges to infinity. Firth's approach to logistic regression is a theoretically sound procedure, which is guaranteed to arrive at finite estimates even in a separation case. Firth's procedure was also proved to significantly reduce the small sample bias of maximum likelihood estimates. The main goal of the paper is to introduce the STATISTICA macro, which performs Firth-type logistic regression.

Page views:: 4821. Submitted: 2011-03-06. Published: 2012-04-17.
Paper: Separation-Resistant and Bias-Reduced Logistic Regression: STATISTICA Macro     Download PDF (Downloads: 6693)
SR_BR_LR.svb: SVB source code for STATISTICA macro Download (Downloads: 935; 16KB) Replication code and data for STATISTICA examples Download (Downloads: 934; 4KB)
v47c02.R: R example code from the paper Download (Downloads: 904; 684B)

DOI: 10.18637/jss.v047.c02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.