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研究生:謝珮琳
研究生(外文):Pai-lin Hsieh
論文名稱:一般化柏拉圖分配其L-moments的估計與抽樣分配
論文名稱(外文):ESTIMATION AND SAMPLING DISTRIBUTIONS OF L-MOMENTS FOR GENERALIZED PARETO DISTRIBUTION
指導教授:唐 正
指導教授(外文):Jen Tang
學位類別:碩士
校院名稱:國立清華大學
系所名稱:統計學研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:51
中文關鍵詞:Edgeworth展開式極端值理論一般化柏拉圖分配L-moments機率加權動差
外文關鍵詞:Edgeworth expansionExtreme Value TheoryGeneralized Pareto DistributionL-momentsProbability-weighted Moments
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  • 被引用被引用:0
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  • 下載下載:26
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以Edgeworth展開式修正來自一般化柏拉圖分配其L-moments統計量的漸近分配
VaR is an important risk management tool in finance and economics, which focuses on tail estimation, especially when financial loss follows a heavy-tailed distribution. It is recently found that Generalized Pareto Distributions (GPDs) from Extreme Value Theory (EVT) perform better than certain existing models, e.g., the lognormal distribution, in describing excessive losses. L-moments, expectations of certain linear combinations of order statistics, can describe a probability distribution well like the conventional moments and are more robust to outliers, because their power measurements are also in cumulative distribution functions.
In this paper, we obtain certain properties of the unbiased estimators of the L-moments, namely the UMVU property. We then obtain Edgeworth expansions for the sampling distributions of L-moments when sampling from a generalized Pareto distribution. These Edgeworth approximations are expected to give better approximations than the CLT’s normal approximation, because these Edgeworth approximations add more information about loss severity to describing the distribution through some adjustments of skewness and kurtosis. We will also show how the scale and shape parameters of a GPD may affect the fitting performance of the derived Edgeworth approximation of L-moments, by some numerical analyses. Finally we will illustrate the method using real data from an insurance company.
1.Introduction ------------------------------------1
2.Extreme Value Theory ----------------------------4
2.1The Generalized Extreme Value Distributions ---4
2.2 The Generalized Pareto Distribution ----------7
3.L-moments and Probability-weighted Moments
Estimation -------------------------------------10
3.1 L-moments -----------------------------------10
3.2 Probability-weighted Moments and Their
Unbiased Estimators -------------------------11
3.3 Asymptotic Distributions of Sample PWMs -----13
4.Estimations and Edgeworth Expansions of Sampling
Distributions of L-moments ---------------------14
4.1 L-moments and UMVUEs-------------------------14
4.2 Asymptotic Sampling Distributions of Sample
L-moments -----------------------------------18
4.3 Edgeworth Approximations of the Sampling
Distributions of L-moments ------------------19
5.Numerical Illustration -------------------------27
6.Data Analysis ----------------------------------35
7.Conclusions ------------------------------------39
References ---------------------------------------40
Appendix A ---------------------------------------43
Appendix B ---------------------------------------45
Appendix C ---------------------------------------50
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