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...
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/98306 http://hdl.handle.net/10220/13261 |
| _version_ | 1826115793850466304 |
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| author | Lian, Heng |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Lian, Heng |
| author_sort | Lian, Heng |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T04:01:18Z |
| format | Journal Article |
| id | ntu-10356/98306 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:01:18Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Lian, Heng A note on conditional Akaike information for Poisson regression with random effects |
| title | 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_short | 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 |
| url | https://hdl.handle.net/10356/98306 http://hdl.handle.net/10220/13261 |
| work_keys_str_mv | AT lianheng anoteonconditionalakaikeinformationforpoissonregressionwithrandomeffects AT lianheng noteonconditionalakaikeinformationforpoissonregressionwithrandomeffects |