Barış AŞIKGİL

Information Complexity Criterion for Order Determination in Autoregressive Models

Anahtar Kelimeler:

-

Information Complexity Criterion for Order Determination in Autoregressive Models

Keywords:

-,

PDF

___

Akaike, H. A new look at the statistical model identification, IEEE Transactions on Auto- matic Control, AC-19, 716-723, 1974.
Akaike, H. A Bayesian extension of the minimum AIC procedure of autoregressive model fitting, Biometrika 66 237–242, 1979.
Bozdogan, H. ICOMP: A new model selection criterion, In H.H. Bock (Ed.), Classifica- tion and Related Methods of Data Analysis, North-Holland, CityplaceAmsterdam, 599–608, 1988. [4] Bozdogan, H. Akaike’s information criterion and recent developments in information com- plexity,Journal of Mathematical Psychology 44, 62–91, 2000.
Bozdogan, H. Intelligent statistical data mining with information complexity and genetic algorithms,In H. Bozdogan (Ed.), Statistical Data Mining and Knowledge Discovery, Chap- man and Hall, Boca Raton, Florida, 15–56, 2004.
Burg, J. P. Maximum Entropy Spectral Analysis (PhD Thesis, Department of Geophysics, Stanford University, Stanford, CA, 1975).
Cho, S. H. and Song, I. A formula for computing the autocorrelations of the AR process, The Journal of the Acoustical Society of Korea 15, 4–7, 1996.
Enders, W. Applied Econometric Time Series (John Wiley and Sons, New York, 1995).
Eshel, G. The Yule Walker equations for the AR coefficients, Citeulike-article-id: 763363, 2003. [10] Fuller, W. A. Introduction to Statistical Time Series (Second Edition, John Wiley and Sons, New York, 1996).
Hurvich, C. M. and Tsai, C. L. Regression and time series model selection in small samples, Biometrika 76, 297–307, 1989.