ON DISPARITY BASED GOODNESS-OF-FIT TESTS FOR MULTINOMIAL MODELS

被引：18

作者：

BASU, A

SARKAR, S

机构：

[1] UNIV TEXAS,DEPT MATH,AUSTIN,TX 78712

[2] OKLAHOMA STATE UNIV,DEPT STAT,STILLWATER,OK 74078

来源：

STATISTICS & PROBABILITY LETTERS | 1994年 / 19卷 / 04期

关键词：

BEST ASYMPTOTICALLY NORMAL ESTIMATOR; BLENDED WEIGHT HELLINGER DISTANCE; BLENDED WEIGHT CHI-SQUARE; GOODNESS-OF-FIT; HELLINGER DISTANCE; LIKELIHOOD RATIO TEST; MINIMUM DISPARITY ESTIMATION;

D O I：

10.1016/0167-7152(94)90181-3

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：

A general class of goodness-of-fit tests called disparity tests containing the family of power weighted divergence statistics as a subclass is considered. Under the simple and composite null hypotheses the asymptotic distribution of disparity tests is shown to be chi-square. It is also shown that the blended weight Hellinger distance subfamily, like the power weighted divergence subfamily, has a member that gives an excellent compromise between the Pearson's chi-square and the log likelihood ratio tests.

引用

页码：307 / 312

页数：6

共 11 条

[1] COMPARISON OF X2, -2 LOG R, AND MULTINOMIAL PROBABILITY CRITERIA FOR SIGNIFICANCE TESTS WHEN EXPECTED FREQUENCIES ARE SMALL [J].