Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Autor:	Reiko Aoki, Cibele M. Russo, Dorival Leão-Pinto
Rok vydání:	2009
Předmět:	Statistics and Probability Score test Multivariate statistics Wald test symbols.namesake Modeling and Simulation Likelihood-ratio test Statistics symbols Test statistic Errors-in-variables models Fisher information Mathematics Statistical hypothesis testing
Zdroj:	Communications in Statistics - Simulation and Computation. 38:1447-1469
ISSN:	1532-4141 0361-0918
DOI:	10.1080/03610910902972310
Popis:	Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0b40eab0e878258bc4637ebab6e7f0b4 https://doi.org/10.1080/03610910902972310 Zobrazit plný text záznamu