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Extended Rank Analysis of Covariance as a More Efficient Matched Analysis Considering Trend Information
Author(s) -
Chen ChuChih
Publication year - 2001
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/1521-4036(200111)43:7<895::aid-bimj895>3.0.co;2-8
Subject(s) - covariance , statistic , statistics , rank (graph theory) , mathematics , ranking (information retrieval) , analysis of covariance , matching (statistics) , multivariate statistics , test statistic , econometrics , computer science , statistical hypothesis testing , artificial intelligence , combinatorics
Abstract Classical matched analysis, regarded as analysis of covariance (ANOCOVA) in a broad sense, makes no attempt in modeling and may therefore be inefficient. In this paper, we discuss the relative efficiencies of the ERMP (extended rank and matched‐pair) test ( Chen and Quade , 2000) to standard matched methods, and extend it to the case of multivariate covariables X . Taking advantage of trend information between the response Y and the covariables X by ranking after matching, ERMP test achieves better efficiency than a proposed class of weighted matched statistics. When Y is dichotomous, the optimal weighted matched statistic is equivalent to the Mantel‐Haenszel statistic. Example and simulation results also suggest the conclusion.