极值分布下联合位置与散度模型的变量选择  

Variable Selection in Joint Location and Dispersion Models of the Extreme-value Distribution

作　　者：吴刘仓[1] 李会琼[2] 

机构地区：[1]昆明理工大学理学院,昆明650500 [2]云南大学统计系,昆明650091

出　　处：《工程数学学报》2012年第5期670-680,共11页Chinese Journal of Engineering Mathematics

基　　金：国家自然科学基金(11126309;11026209);云南省自然科学基金(2009ZC039M;2011FB016;2011FZ044);昆明理工大学博士科研启动基金(2009-024)~~

摘　　要：极值分布在地震、洪灾和其它自然灾害的预测中是非常有用的.在许多应用方面,很有必要对散度建模.本文推广经典极值回归模型,研究了联合位置与散度模型,并提出了一种同时对位置模型和散度模型的变量选择方法.同时证明了惩罚极大似然估计具有相合性和oracle性质,通过随机模拟研究了所提出方法的有限样本性质.The extreme-value distribution is very useful in predicting the probability that an extreme earthquake, flood or other natural disaster will occur. In many applications, there is a great need to model the dispersion. In this paper, a unified procedure is proposed to simultaneously select signifi- cant variables in joint location and dispersion models which provide a useful extension of the general extreme-value regression model. It is further shown that the presented penalized maximum likelihood estimator enjoys the consistency and the oracle property. Numerical simulation is conducted to exam- ine the finite sample properties of the proposed method.

关 键 词：异方差模型 联合位置与散度模型 惩罚极大似然估计 变量选择 估计理论 

分 类 号：O212.1[理学—概率论与数理统计]