| 研究生: |
陳宗雄 |
|---|---|
| 論文名稱: |
相關變數之隨機修剪L : 統計量之漸近性 On the asymptotic behavior of randomly trimmed L-statistics with dependent random variables |
| 指導教授: | 吳柏林 |
| 學位類別: |
碩士
Master |
| 系所名稱: |
理學院 - 應用數學系 Department of Mathematical Sciences |
| 論文出版年: | 1991 |
| 畢業學年度: | 79 |
| 語文別: | 英文 |
| 論文頁數: | 35 |
| 外文關鍵詞: | Random trimming, L–statistics, absolutely regular |
| 相關次數: | 點閱:170 下載:0 |
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摘要
本文主要在探討絕對正則隨機變數序列的隨機修剪L統計量的漸近性,當修剪係系數收斂至a和b時(O<a<b<l),對它們的分配函數限制並不多;然而當a=0及b=1 時,則限制的條件須更加嚴格,這也就是為什麼我們要做隨機修剪的主要原因。同時,由於大部分的時間序列模式都是絕對正則的隨機變數序列,這也是研究本文的主要動機之一。
本文是想嘗試著把G. R. Shorack (1989)的論文隨機修剪L統計量,推廣,把該文中立相獨立的隨機變數序列換成絕對正則的隨機變數序列。在這同時,我們必需將一些經驗累積分配函數的不等式推廣,推廣過程中將重覆使用Yoshihara (1978) 的機率不等式。
ABSTRACT
We will prove central limit theorem for randomly trimmed L-statistics with absolutely regular random variables. When the fractions trimmed converge to a and l-b, (with 0<a<b<l) there are little restrictions on the df's of the r.v.'s, - but the limiting r.v. has several contributing terms, making the studentization complicated unless the trimming fractions converge fast enough.
For a=0 and b=l, the restriction on the rate of convergence of the trimming fractions is more severe, however this is a most reasonable way to trim.
Contents
1. Introduction 1
2 .Preliminaries 7
3 .Asymptotic Normality of the Leading Term 12
4 .Asymptotic Negligibility of the Remainder Terms 19
5 .Appendix 25
6 .References 34
REFREENCES
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[2] Billingsley, P. (1968). Convergence of Probability Measure. Wiley,New York.
[3] Deo, C.M. (1973). A note on empirical processes of strong-mixing sequences. Ann. Probab. 1, 870-875.
[4] Mason, D.M. (981). Bounds for weighted empirical distribution functions.Ann. Probab. 9, 881-884.
[5] Pham, T.D. and Tran, Tran, L.T. (1982). On functions of order statistics in the non-LLd. case. Sankhya, A, 44, 225-26l.
[6] Pham, T.D. and Tran, L.T. (1985). Some strong mixing properties of time series models. Stochastic Processes and their applications. 19,297-303.
[7] Puri, M.L. and Tran, L. T. (980). Empirical distributions functions and functions of order statistics for mixing random variables . J.Multi. analy. 10, 405-425.
[8] Serfling, R.J. (1980). Approximation Theorems of Mathematics
Statistics. Wiley, New York.
[9] Shorack, G. (1989). Randomly trimmed L-statistics. JSPI. 21, 293 - 304.
[10] Shorack, G. and Wellner (1986). EmpiricaL Processes with applications
to Statistics. Wiley, New York.
[11] Wu, Berlin. (1988). On order statistics in time series analysis. Ph.D
Thesis, Indiana University, U. S.A.
[12] Yoshihara, K. (1978). Probability inequalities for sums of absolutely regular processes and their applications. Z. Wahrsch Verw. Geb. 43,319-330.
[13] Zuijlen, M.C.A. Van. (1976). Some properties of empirical distribution functions in the non-i.i.d. case. Ann. Statist. 5, 406 - 408.
[l4] Zuijlen, M.C.A. Van. (1978). Properties of the empirical distribution function for independent . nonidentically distributed random variables.Ann. Probab. 6, 250-266.
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