Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/89768
題名: 參數化Markov鏈上的自助法
Bootstrapping Markov chains for parametric case
作者: 何秀敏
貢獻者: 傅承德
何秀敏
日期: 1991
上傳時間: 2-May-2016
摘要: 設{ Xn ; n ? o } 為一不可約,單一週期及正常返(irreducible, aperiodicand positive recurrent) 的Markov 鏈,令其狀態空間(state space) S 為有限及移轉矩陣(transitionprobability matrix) P. 假設Pij’S 是某一個未知參數θ的函數,且此函數為已知,稱之為PjJ (θ) ,其值域屬於RK ,並令π是它的平接機率(stationary probability) ,給定一組己知的觀察值{ Xn; 0 ?j? n },令θn 為θ 的最大概似估計(maxirnumlikelihood estimator). 在本論文中,我們要利用自助法
參考文獻: References\r\n\r\n1. Anderson, T. and Goodman, L. (1957). Statistical inference about Markov chains.\r\nAnn. Math. Statist. 28, pp. 89-110.\r\n2. Athreya, K. B. and Fuh, C. D. (1989). Bootstrapping Markov chains: Countable\r\ncase. Technical Report: B-89-7, Institute of Statistical Science, Academia Sincia,\r\nTaipei, Taiwan, ROC.\r\n3. Bartlett, M. S. (1951). The frequency goodness of fit test for probability chains. Proc.Carnb. Phil. Soc. 47, pp. 86-95.\r\n4. Basawa, I. V. and Prakasa Rao, B. L. S. (1980). Statistical inference for stochastic\r\nprocesses. Academic Press, New York.\r\n5. Billingsley, P. (1961). Statistical inference for Markov Processes. The University of\r\nChicago Press, Chicago.\r\n6. Cinlar, E. (1975). Introduction to Stochastic Processes. pp. 106-168. Prentice-Hall,\r\nEnglewwood Cliffs, N. J.\r\n7. Doob, J. L. (1953). Stochastic processes. p.228 and p.232. Springer- Verlag, Berlin.\r\n8. Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans. SIAM,\r\nPhiladelphia.\r\n9. Kulperger, R. J. and Prakasa Rao, B. L. S. (1990). Bootstrapping a finite state\r\nMarkov chains. Sankhya A 51, Pt. 2, pp. 178-191.\r\n10. Kemeny, J. G. and Snell, J. L. (1960). Finite Markov chains. pp. 69-71. SpringerVerlage,New York.\r\n11. Rao, C. R. (1973). Linear statistkal inference and its applications. pp. 363-366.\r\nWieley: New York.
描述: 碩士
國立政治大學
應用數學系
資料來源: http://thesis.lib.nccu.edu.tw/record/#B2002005104
資料類型: thesis
Appears in Collections:學位論文

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