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

JOURNAL OF MULTIVARIATE ANALYSIS

卷:54

Article

MENENDEZ, M ; MORALES, D ; PARDO, L ; VAJDA, I

关键词: STATISTICAL CLASSIFICATION;    CATEGORICAL DATA;    CLUSTERED DATA;    MINIMUM DIVERGENCE ESTIMATION;    MINIMUM DIVERGENCE TESTING;    ASYMPTOTIC THEORY;    OPTIMALITY OF TESTING;   

DOI  :  10.1006/jmva.1995.1060

来源: Elsevier

【 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.

【 授权许可】

Free