Distance-Based Estimation Methods for Models for Discrete and Mixed-Scale Data

Autor:	Elisavet M. Sofikitou, Ray Liu, Huipei Wang, Marianthi Markatou
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	contingency tables disparity mixed-scale data pearson residuals residual adjustment function robustness Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 23, Iss 1, p 107 (2021)
Druh dokumentu:	article
ISSN:	1099-4300
DOI:	10.3390/e23010107
Popis:	Pearson residuals aid the task of identifying model misspecification because they compare the estimated, using data, model with the model assumed under the null hypothesis. We present different formulations of the Pearson residual system that account for the measurement scale of the data and study their properties. We further concentrate on the case of mixed-scale data, that is, data measured in both categorical and interval scale. We study the asymptotic properties and the robustness of minimum disparity estimators obtained in the case of mixed-scale data and exemplify the performance of the methods via simulation.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/8ddf33a5a87442889ffc192de834f867 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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