Variational Inference for Count Response Semiparametric Regression
| Autor: | Jan Luts, Matt P. Wand |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Mixed model Statistics::Theory mean field variational Bayes Statistics & Probability penalized splines Bayesian probability Negative binomial distribution Poisson distribution Methodology (stat.ME) symbols.namesake Statistics Variational message passing Applied mathematics Statistics::Methodology Semiparametric regression real-time semiparametric regression Statistics - Methodology Mathematics Applied Mathematics Semiparametric model approximate Bayesian inference generalized additive mixed models symbols Bayesian linear regression |
| Zdroj: | Bayesian Anal. 10, no. 4 (2015), 991-1023 |
| Popis: | Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e. a non-negative integer. We treat both the Poisson and Negative Binomial families as models for the response variable. Our approach utilizes recently developed methodology known as non-conjugate variational message passing. For concreteness, we focus on generalized additive mixed models, although our variational approximation approach extends to a wide class of semiparametric regression models such as those containing interactions and elaborate random effect structure. Comment: 19 pages, 7 figures |
| Databáze: | OpenAIRE |
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