Heteroscedasticity and/or Autocorrelation Checks in Longitudinal Nonlinear Models with Elliptical and AR(1) Errors  

作　　者：Chun-Zheng CAO Jin-Guan LIN 

机构地区：[1]College of Math&Physics,Nanjing University of Information Science&Technology,Nanjing 210044,China [2]Department of Mathematics,Southeast University,Nanjing 210096,China

出　　处：《Acta Mathematicae Applicatae Sinica》2012年第1期49-62,共14页应用数学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 11171065 and NSFJSBK2011058)

摘　　要：The aim of this paper is to study the tests for variance heterogeneity and/or autocorrelation in nonlinear regression models with elliptical and AR（1） errors. The elliptical class includes several symmetric multivariate distributions such as normal, Student-S, power exponential, among others. Several diagnostic tests using score statistics and their adjustment are constructed. The asymptotic properties, including asymptotic chi-squave and approximate powers under local alternatives of the score statistics, are studied. The properties of test statistics are investigated through Monte Carlo simulations. A data set previously analyzed under normal errors is reanalyzed under elliptical models to illustrate our test methods.The aim of this paper is to study the tests for variance heterogeneity and/or autocorrelation in nonlinear regression models with elliptical and AR（1） errors. The elliptical class includes several symmetric multivariate distributions such as normal, Student-S, power exponential, among others. Several diagnostic tests using score statistics and their adjustment are constructed. The asymptotic properties, including asymptotic chi-squave and approximate powers under local alternatives of the score statistics, are studied. The properties of test statistics are investigated through Monte Carlo simulations. A data set previously analyzed under normal errors is reanalyzed under elliptical models to illustrate our test methods.

关 键 词：AUTOCORRELATION elliptical distributions HETEROSCEDASTICITY longitudinal data nonlinear model score test 

分 类 号：O212.1[理学—概率论与数理统计] TU245.2[理学—数学]