DIVERGENCE-BASED ESTIMATION AND TESTING OF STATISTICAL-MODELS OF CLASSIFICATION

被引：20
|
作者：
MENENDEZ, M
MORALES, D
PARDO, L
VAJDA, I
机构：
[1] UNIV COMPLUTENSE MADRID,DEPT STAT & OPERAT RES,E-28040 MADRID,SPAIN
[2] ACAD SCI CZECH REPUBL,INST INFORMAT THEORY & AUTOMAT,CR-18208 PRAGUE,CZECH REPUBLIC
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 1995年 / 54卷 / 02期
关键词：
STATISTICAL CLASSIFICATION; CATEGORICAL DATA; CLUSTERED DATA; MINIMUM DIVERGENCE ESTIMATION; MINIMUM DIVERGENCE TESTING; ASYMPTOTIC THEORY; OPTIMALITY OF TESTING;
D O I：
10.1006/jmva.1995.1060
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：
The problems of estimating parameters of statistical models for categorical data, and testing hypotheses about these models are studied. Asymptotic properties of estimators minimizing phi-divergence between theoretical and empirical vectors of means are established. Asymptotic distributions of phi-divergences between empirical and estimated vectors of means are explictly evaluated, and tests based on these statistics are studied. The paper extends results previously established in this area. (C) 1995 Academic Press, Inc.
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页码：329 / 354
页数：26
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