Binary data regression: Weibull distribution

Renault Caron, Adriano Polpo

Research output: Chapter in Book/Conference paperConference paperpeer-review

2 Citations (Scopus)

Abstract

The problem of estimation in binary response data has receivied a great number of alternative statistical solutions. Generalized linear models allow for a wide range of statistical models for regression data. The most used model is the logistic regression, see Hosmer et al. [6]. However, as Chen et al. [5] mentions, when the probability of a given binary response approaches 0 at a different rate than it approaches I, symmetric linkages are inappropriate. A class of models based on Weibull distribution indexed by three parameters is introduced here. Maximum likelihood methods are employed to estimate the parameters. The objective of the present paper is to show a solution for the estimation problem under the Weibull model. An example showing the quality of the model is illustrated by comparing it with the alternative probit and logit models.

Original languageEnglish
Title of host publicationBAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING
EditorsPM Goggans, CY Chan
PublisherAmerican Institute of Physics
Pages187-193
Number of pages7
ISBN (Print)978-0-7354-0729-9
DOIs
Publication statusPublished - 2009
Event29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Oxford, United States
Duration: 5 Jul 200910 Jul 2009
http://inspirehep.net/record/980193

Publication series

NameAIP Conference Proceedings
PublisherAMER INST PHYSICS
Volume1193
ISSN (Print)0094-243X

Conference

Conference29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Abbreviated titleMaxEnt 2009
Country/TerritoryUnited States
CityOxford
Period5/07/0910/07/09
Internet address

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