Second-order bias-corrected AIC in multivariate normal linear models under non-normality
✍ Scribed by Hirokazu Yanagihara; Ken-Ichi Kamo; Tetsuji Tonda
- Publisher
- John Wiley and Sons
- Year
- 2011
- Tongue
- French
- Weight
- 353 KB
- Volume
- 39
- Category
- Article
- ISSN
- 0319-5724
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✦ Synopsis
This paper deals with a bias correction of Akaike's information criterion (AIC) for selecting variables in multivariate normal linear regression models when the true distribution of observation is an unknown non-normal distribution. It is well known that the bias of AIC is O(1), and there are a number of the first-order bias-corrected AICs which improve the bias to O(n -1 ), where n is the sample size. A new information criterion is proposed by slightly adjusting the first-order bias-corrected AIC. Although the adjustment is achieved by merely using constant coefficients, the bias of the new criterion is reduced to O(n -2 ). Then, a variance of the new criterion is also improved. Through numerical experiments, we verify that our criterion is superior to others.