Moment-independent importance measure of basic random variable and its probability density evolution solution

作     者：CUI LiJie,Lü ZhenZhou & ZHAO XinPan School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China 

作者机构：School of Aeronautics Northwestern Polytechnical University Xi’an China 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2010年第53卷第4期

页      面：1138-1145页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by the National Natural Science Foundation of China (Grant Nos NSFC1057211, 50875213) New Century Excellent Talents in University of China (Grant No NCET-05-0868) Aviation Science Foundation of China (Grant No 2007ZA53012) National High Technology Research and Development Program of China (Grant No 2007AA04Z401) the Important National Science & Technology Specific Projects (Grant No 2009ZX04014-015-03) 

主　　题：basic random variable moment-independent importance measure probability density evolution method 

摘      要：To analyze the effect of basic variable on failure probability in reliability analysis,a moment-independent importance measure of the basic random variable is proposed,and its properties are analyzed and *** on this work,the importance measure of the basic variable on the failure probability is compared with that on the distribution density of the *** use of the probability density evolution method,a solution is established to solve two importance measures,which can efficiently avoid the difficulty in solving the importance *** numerical examples and engineering examples are used to demonstrate the proposed importance measure on the failure probability and that on the distribution density of the *** results show that the proposed importance measure can effectively describe the effect of the basic variable on the failure probability from the distribution density of the basic ***,the results show that the established solution on the probability density evolution is efficient for the importance measures.