On bias correction of the akaike information criterion in linear models

Communications in Statistics - Theory and Methods

Volumn 25, Issue 8, 1996, Pages 1845-1857

  Noda, Kazuo ^a   Miyaoka, Etsuo ^a   Itoh, Masashi ^b  

^a TOKYO UNIVERSITY OF SCIENCE   (Japan)

^b The Tokyo Metropolitan Research Institute for Environmental Protection   (Japan)

Author keywords

AIC; Asymptotic biasedness; Bias corrected AIC; Consistency; Kullback Leibler Information; Linear models; Optimum parameter value

Indexed keywords

EID: 0039380085     PISSN: 03610926     EISSN: None     Source Type: Journal    
DOI: 10.1080/1532415X.1996.11877458     Document Type: Article

References (8)

1. Akaike,H. (1973) Information theory and an extension of the maximum likelihood principle; 2nd International Symposium on Information Theory, B.N.Petrov and F.Csaski, eds, Akademiai Kaidom Budapest, 716-723.
2. Arnold, S. (1981) The Theory of Linear Models and Multivariate Analysis, John Wiley, New York.
3. Bancroft, T.A. (1944) On biases in estimation due to the use of preliminary tests of significance, Ann. Math. Statist., 15, 190-204.
4. Hirano, K. (1977) Estimation procedures based on preliminary test, shrinkage technique and information criterion, Ann. Inst. Statist. Math., 29, Part A, 21-34.
5. Kullback, S. and Leibler, R.A. (1951) On information and sufficiency, Ann. Math. Statist., 22, 79-86.
6. Noda, K. and Itoh, M. (1993) On stability of two-phase segmented regression model selection, Science Univ. of Tokyo J. of Math., 29, 1, 113-141.
7. Sugiura, N. (1978) Further analysis of the data by Akaike's information criterion and the finite corrections, Commun. Statist.-Theor. Meth.,A7(1), 13-26.
8. Wang, Z.X. and Guo, D.R. (1989) Specail function, World Scientific, New Jersey.