Abstract
This paper studies a class of AIC-like model selection criteria for a generalized linear model with the canonical link. They have the form of log L - p * C, where log L is the maximized log-likelihood, p is the number of parameters and C is a term depending on the sample size n and satisfying C/n → 0 and C/log log n → ∞ as n → ∞. Under suitable conditions, this class of criteria is shown to be strongly consistent. A simulation study was also conducted to assess the finite-sample performance with various choices of C for variable selection in a logit model and a log-linear model.
Original language | English (US) |
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Pages (from-to) | 371-382 |
Number of pages | 12 |
Journal | Statistics and Probability Letters |
Volume | 71 |
Issue number | 4 |
DOIs | |
State | Published - Mar 15 2005 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
Keywords
- Canonical link function
- Generalized linear model
- Information theoretic criteria
- Model selection