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 language | English (US) |
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Pages (from-to) | 475-486 |
Number of pages | 12 |
Journal | Communications in Statistics: Simulation and Computation |
Volume | 39 |
Issue number | 3 |
DOIs | |
State | Published - 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