頁籤選單縮合
題名 | Information and Prediction Criteria for Model Selection in Stochastic Regression and ARMA Models |
---|---|
作者姓名(外文) | Lai,Tze Leung; LeeChang Ping; | 書刊名 | Statistica Sinica |
卷期 | 7:2 1997.04[民86.04] |
頁次 | 頁285-309 |
分類號 | 319.5 |
關鍵詞 | 費雪訊息; 累積預期誤差; Accumulated prediction error criterion; BIC; Fisher information criterion; FPE; Kullback-Leibler information; |
語文 | 英文(English) |
英文摘要 | After a brief review of several information-based and prediction-based model selection criteria, we extend Rissanen's accumulated prediction error criterion and Wei's fisher information criterion (FIC) from linear to general stochastic regression models, which include ARMA models and nonlinear ARX models in time series analysis as special cases. Strong consistency of these model selection criteria is established under certain conditions and the FIC is also shown to be an asymptotic approximation to some Bayes procedure. The special case of ARMA models is then studied in detail, and theoretical analysis and simulation results show that the FIC compares favorably with other procedures in the literature. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。