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| Title: | 從假設檢定的觀點探討ARMA模型的參數配適
ARMA Model Selection from Hypothesis Point of View |
| Authors: | 林芸生
Lin, Yun Sheng |
| Contributors: | 黃子銘
Huang, Tzee Ming
林芸生
Lin, Yun Sheng |
| Keywords: | 假設檢定
ARMA
Model selection
AIC
BIC
Hypothesis testing |
| Date: | 2009 |
| Issue Date: | 2013-09-05 15:11:08 (UTC+8) |
| Abstract: | 本篇論文著重於探討ARMA模型的選模準則，過去較為著名的AIC、BIC等選模準則中，若總參數個數相同，模型選擇便簡化為比較各模型的概似函數在MLE下的值，故本研究將假設檢定定義為檢定總參數個數；截至目前為止，選模準則在使用上以AIC及BIC較為普遍，此兩種選模準則從本研究所定義的假設檢定的觀點來看，AIC犯型一誤差機率高，同時檢定力也高；BIC犯型一誤差的機率極低，同時檢定力也相對不高，本研究從此觀點提出一個選模準則方法，嘗試將上述兩種方法折衷，將型一誤差控制在5%，且檢定力略高於BIC。模擬的結果在理想的情形下皆符合預期，但在真實情形本研究方法涉及第一階段的模型選取，本研究提供兩種第一階段的模型選取方法，模擬的結果顯示，方法一型一誤差略為膨脹，檢定力增幅顯著；方法二型一誤差控制精準，但檢定力表現較差。本研究所提出的方法計算時間較為冗長，但若想將 AIC 及 BIC 方法折衷，可考慮嘗試本研究方法。
This thesis focuses on model selection criteria for ARMA models. For information-based criteria such as AIC and BIC, the task of model selection is reduced to the comparison among likelihood values at maximum likelihood estimates if the numbers of parameters in candidate models are all the same. Thus the key step in model selection is the determination of the total number of parameters.
The determination of number of parameters can be addressed using a hypothesis testing approach, where the null hypothesis is that the total number of model parameters is equal to a given number k and the alternative hypothesis is that the total number of parameters is equal to k+1. In this thesis, an information-based model selection method is proposed, where the number of parameters is determined using a two-stage testing procedure, which is constructed with the attempt to control the average type I error probability to be 5%. When using BIC in the above testing problem, simulation results indicate that the average type I error probability for BIC is lower than 0.05, so it is expected the proposed test is more powerful than BIC.
The first stage of the proposed test involves selecting the most likely models under the null and the alternative hypothesis respectively. Two methods are considered for the first-stage selection. For the first method, the type I error probability can be larger than 0.05, but the power is significantly larger than BIC. For the second method, the type I error probability is under control, but its power increment is comparatively low. The computing time for the proposed test is rather long. However, for those who need an eclectic method between AIC and BIC, the proposed test can serve as a reasonable choice. |
| Reference: | Akike, H. (1973), “Information theory and an extension of the maximum likelihood principle”, Budapest, Hungary, 267-281
Akike, H. (1978), “A Bayesian analysis of the minimum AIC procedure”, Annals of the Institute of Statistical mathematics, 30(1), 9-14
Brockwell, P.J. and Davis, R.A. (2009), “Time series: theory and methods”, Springer Verlag
Casella, G. and Berger, R.L. and Berger, R.L. (2002), “Statistical inference”, Duxbury Pacific Grove, CA
Clarke, B. (2001), “Combining model selection procedures for online prediction”, Sankhy: The Indian Journal of Statistics, Series A, 229--249
Hurvich, C.M. and Tsai, C.L. (1989), “Regression and time series model selection in small samples”, Biometrika, 76(2), 297
Kullback, S. and Leibler, RA (1951), “On information and sufficiency”, The Annals of Mathematical Statistics, 79-86
Reschenhofer , Erhard (2005), “Selecting Selection Methods”, InterStat: Statistics on the Internet, 7(3), 1-17
Wagenmakers, E.J. and Grunwald, P. and Steyvers, M. (2006), “Accumulative prediction error and the selection of time series models”, Journal of Mathematical Psychology, 50(2), 149-166
Yang, Y. (2005), “Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation”, Biometrika, 92(4), 937
溫志宏, 無線通道模型概論, 國立中正大學
葉欣甯(2002), 時間序列的選模分析：Cross Validation之應用, 國立清華大學碩士論文 |
| Description: | 碩士
國立政治大學
統計研究所
97354017
98 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0097354017 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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