Multivariate Two-Sided Tests for Normal Mean Vectors with Unknown Covariance Matrix

Autor:	Tsunehisa Imada
Rok vydání:	2013
Předmět:	Statistics and Probability Multivariate statistics Covariance matrix Scatter matrix Modeling and Simulation Orthographic projection Statistics Matrix t-distribution Matrix normal distribution Multivariate normal distribution Normal-Wishart distribution Mathematics
Zdroj:	Communications in Statistics - Simulation and Computation. 42:183-201
ISSN:	1532-4141 0361-0918
DOI:	10.1080/03610918.2011.633727
Popis:	In this study, we discuss two kinds of multivariate two-sided tests for normal mean vectors with unknown covariance matrix. First, assuming that all components of a normal mean vector are simultaneously nonnegative or non positive, we consider a multivariate two-sided test for testing whether the normal mean vector is equal to zero or not. Next, assuming that all differences of components between two normal mean vectors are simultaneously non negative or non positive, we consider a multivariate two-sided test for testing whether the two normal mean vectors are equal or not. We construct methods for testing by referring to Glimm et al. (2002), Tamhane and Logan (2002) and Sasabuchi (2007). Finally, we give some simulation results regarding critical values and power of the test intended to compare the three methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2c95328b57c413d31bce236d43f03117 https://doi.org/10.1080/03610918.2011.633727 Zobrazit plný text záznamu