Semiparametric negative binomial regression models

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Negative-binomial (NB) regression models have been widely used for analysis of count data displaying substantial overdispersion (extra-Poisson variation). However, no formal lack-of-fit tests for a postulated parametric model for a covariate effect have been proposed. Therefore, a flexible parametric procedure is used to model the covariate effect as a linear combination of fixed-knot cubic basis splines or B-splines. Within the proposed modeling framework, a log-likelihood ratio test is constructed to evaluate the adequacy of a postulated parametric form of the covariate effect. Simulation experiments are conducted to study the power performance of the proposed test.

Original languageEnglish (US)
Pages (from-to)475-486
Number of pages12
JournalCommunications in Statistics: Simulation and Computation
Volume39
Issue number3
DOIs
StatePublished - Mar 2010

Keywords

  • B-spline
  • Lack-of-fit test
  • Log-likelihood ratio test
  • Negative binomial
  • Overdispersion
  • Profile likelihood

ASJC Scopus subject areas

  • Modeling and Simulation
  • Statistics and Probability

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