Complementary log regression for generalized linear models

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Use and implementation of the complementary log regression model are discussed, integrating various separate applications of the model under the form of a generalized linear model. Some motivation is drawn from cases where an underlying random variable is reduced to a dichotomous form. Estimation and testing are facilitated by recognizing the complementary log as a specific link function within a generalized linear framework. Testing for goodness of link via efficient scores is also discussed.

Original languageEnglish (US)
Pages (from-to)94-99
Number of pages6
JournalAmerican Statistician
Volume46
Issue number2
DOIs
StatePublished - 1992
Externally publishedYes

Fingerprint

Generalized Linear Model
Regression
Testing
Link Function
Regression Model
Random variable
Form
Generalized linear model
Model
Framework
Regression model
Random variables

Keywords

  • Binomial model
  • Data truncation
  • Extended link family
  • Goodness-of-link testing
  • Logistic regression
  • Nonlinear regression

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Complementary log regression for generalized linear models. / Piegorsch, Walter W.

In: American Statistician, Vol. 46, No. 2, 1992, p. 94-99.

Research output: Contribution to journalArticle

@article{033f94019a074c61b461490edbb93428,
title = "Complementary log regression for generalized linear models",
abstract = "Use and implementation of the complementary log regression model are discussed, integrating various separate applications of the model under the form of a generalized linear model. Some motivation is drawn from cases where an underlying random variable is reduced to a dichotomous form. Estimation and testing are facilitated by recognizing the complementary log as a specific link function within a generalized linear framework. Testing for goodness of link via efficient scores is also discussed.",
keywords = "Binomial model, Data truncation, Extended link family, Goodness-of-link testing, Logistic regression, Nonlinear regression",
author = "Piegorsch, {Walter W}",
year = "1992",
doi = "10.1080/00031305.1992.10475858",
language = "English (US)",
volume = "46",
pages = "94--99",
journal = "American Statistician",
issn = "0003-1305",
publisher = "American Statistical Association",
number = "2",

}

TY - JOUR

T1 - Complementary log regression for generalized linear models

AU - Piegorsch, Walter W

PY - 1992

Y1 - 1992

N2 - Use and implementation of the complementary log regression model are discussed, integrating various separate applications of the model under the form of a generalized linear model. Some motivation is drawn from cases where an underlying random variable is reduced to a dichotomous form. Estimation and testing are facilitated by recognizing the complementary log as a specific link function within a generalized linear framework. Testing for goodness of link via efficient scores is also discussed.

AB - Use and implementation of the complementary log regression model are discussed, integrating various separate applications of the model under the form of a generalized linear model. Some motivation is drawn from cases where an underlying random variable is reduced to a dichotomous form. Estimation and testing are facilitated by recognizing the complementary log as a specific link function within a generalized linear framework. Testing for goodness of link via efficient scores is also discussed.

KW - Binomial model

KW - Data truncation

KW - Extended link family

KW - Goodness-of-link testing

KW - Logistic regression

KW - Nonlinear regression

UR - http://www.scopus.com/inward/record.url?scp=0344930426&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0344930426&partnerID=8YFLogxK

U2 - 10.1080/00031305.1992.10475858

DO - 10.1080/00031305.1992.10475858

M3 - Article

AN - SCOPUS:0344930426

VL - 46

SP - 94

EP - 99

JO - American Statistician

JF - American Statistician

SN - 0003-1305

IS - 2

ER -