Unified Asymptotic Results for Maximum Spacing and Generalized Spacing Methods for Continuous Models

作     者：Andrew Luong 

作者机构：école d’Actuariat Université Laval Québec Canada 

出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)

年 卷 期：2018年第8卷第3期

页      面：614-639页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Maximum Product of Spacings M-Estimators Quasi-Likelihood Ratio Test Statistic α-Mixing Sequences 

摘      要：Asymptotic results are obtained using an approach based on limit theorem results obtained for α-mixing sequences for the class of general spacings (GSP) methods which include the maximum spacings (MSP) method. The MSP method has been shown to be very useful for estimating parameters for univariate continuous models with a shift at the origin which are often encountered in loss models of actuarial science and extreme models. The MSP estimators have also been shown to be as efficient as maximum likelihood estimators in general and can be used as an alternative method when ML method might have numerical difficulties for some parametric models. Asymptotic properties are presented in a unified way. Robustness results for estimation and parameter testing results which facilitate the applications of the GSP methods are also included and related to quasi-likelihood results.