Goodness-of-fit tests for semiparametric and parametric hypotheses based on the probability weighted empirical characteristic function

被引：7

作者：

Meintanis, Simos G. ^{[1
,2
]}

Allison, James ^{[2
]}

Santana, Leonard ^{[2
]}

机构：

[1] Univ Athens, Dept Econ, Athens, Greece

[2] North West Univ, Unit Business Math & Informat, Potchefstroom, South Africa

来源：

STATISTICAL PAPERS | 2016年 / 57卷 / 04期

基金：

新加坡国家研究基金会;

关键词：

Characteristic function; Empirical characteristic function; Goodness-of-fit test; Mixed model; Multivariate normal distribution; Test for symmetry; NONPARAMETRIC REGRESSION-MODELS; SYMMETRIC ERROR DISTRIBUTION; MULTIVARIATE NORMALITY; MIXED MODELS;

D O I：

10.1007/s00362-016-0760-0

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We investigate the finite-sample properties of certain procedures which employ the novel notion of the probability weighted empirical characteristic function. The procedures considered are: (1) Testing for symmetry in regression, (2) Testing for multivariate normality with independent observations, and (3) Testing for multivariate normality of random effects in mixed models. Along with the new tests alternative methods based on the ordinary empirical characteristic function as well as other more well known procedures are implemented for the purpose of comparison.

引用

页码：957 / 976

页数：20

共 34 条

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