Bayesian analysis of generalized partially linear single-index models
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AbstractWe extend generalized partially linear single-index models by incorporating a random residual effect into the nonlinear predictor so that the new models can accommodate data with overdispersion. Based on the free-knot spline techniques, we develop a fully Bayesian method to analyze the proposed models. To make the models spatially adaptive, we further treat the number and positions of spline knots as random variables. As random residual effects are introduced, many of the completely conditional posteriors become standard distributions, which greatly facilitates sampling. We illustrate the proposed models and estimation method with a simulation study and an analysis of a recreational trip data set. (C) 2013 Elsevier B.V. All rights reserved.
All Author(s) ListPoon WY, Wang HB
Journal nameComputational Statistics and Data Analysis
Year2013
Month12
Day1
Volume Number68
PublisherELSEVIER SCIENCE BV
Pages251 - 261
ISSN0167-9473
eISSN1872-7352
LanguagesEnglish-United Kingdom
KeywordsFree-knot spline; Generalized linear model; Gibbs sampler; Overdispersion; Reversible jump Markov chain Monte Carlo; Single-index model
Web of Science Subject CategoriesComputer Science; Computer Science, Interdisciplinary Applications; COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS; Mathematics; Statistics & Probability; STATISTICS & PROBABILITY

Last updated on 2021-25-01 at 01:14