A note on conditional Akaike information for Poisson regression with random effects

A popular model selection approach for generalized linear mixed- effects models is the Akaike information criterion, or AIC. Among others, [7] pointed out the distinction between the marginal and conditional infer- ence depending on the focus of research. The conditional AIC was derived for the...

Full description

Saved in:
Bibliographic Details
Main Author: Lian, Heng
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2013
Online Access:https://hdl.handle.net/10356/98306
http://hdl.handle.net/10220/13261
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-98306
record_format dspace
spelling sg-ntu-dr.10356-983062020-03-07T12:34:46Z A note on conditional Akaike information for Poisson regression with random effects Lian, Heng School of Physical and Mathematical Sciences A popular model selection approach for generalized linear mixed- effects models is the Akaike information criterion, or AIC. Among others, [7] pointed out the distinction between the marginal and conditional infer- ence depending on the focus of research. The conditional AIC was derived for the linear mixed-effects model which was later generalized by [5]. We show that the similar strategy extends to Poisson regression with random effects, where conditional AIC can be obtained based on our observations. Simulation studies demonstrate the usage of the criterion. 2013-08-29T07:47:06Z 2019-12-06T19:53:23Z 2013-08-29T07:47:06Z 2019-12-06T19:53:23Z 2012 2012 Journal Article Lian, H. (2012). A note on conditional Akaike information for Poisson regression with random effects. Electronic Journal of Statistics, 6(0), 1-9. 1935-7524 https://hdl.handle.net/10356/98306 http://hdl.handle.net/10220/13261 10.1214/12-EJS665 en Electronic journal of statistics
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
description A popular model selection approach for generalized linear mixed- effects models is the Akaike information criterion, or AIC. Among others, [7] pointed out the distinction between the marginal and conditional infer- ence depending on the focus of research. The conditional AIC was derived for the linear mixed-effects model which was later generalized by [5]. We show that the similar strategy extends to Poisson regression with random effects, where conditional AIC can be obtained based on our observations. Simulation studies demonstrate the usage of the criterion.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Lian, Heng
format Article
author Lian, Heng
spellingShingle Lian, Heng
A note on conditional Akaike information for Poisson regression with random effects
author_sort Lian, Heng
title A note on conditional Akaike information for Poisson regression with random effects
title_short A note on conditional Akaike information for Poisson regression with random effects
title_full A note on conditional Akaike information for Poisson regression with random effects
title_fullStr A note on conditional Akaike information for Poisson regression with random effects
title_full_unstemmed A note on conditional Akaike information for Poisson regression with random effects
title_sort note on conditional akaike information for poisson regression with random effects
publishDate 2013
url https://hdl.handle.net/10356/98306
http://hdl.handle.net/10220/13261
_version_ 1681038064139370496