An optimal test for the mean function hypothesis

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27789

Title:	An optimal test for the mean function hypothesis
Authors:	Cheng,KF;Wu,JW
Contributors:	統計研究所
Keywords:	GENERALIZED LINEAR-MODELS;REGRESSION-MODELS
Date:	1998
Issue Date:	2010-06-29 19:33:39 (UTC+8)
Publisher:	中央大學
Abstract:	The conditional mean of the response variable Y given the covariates X = x is an element of R-p is usually modelled by a parametric function g(beta x), where g(.) is a known function and beta is a row vector of p unknown parameters. In this paper, a new method for testing the goodness of fit of the model g(beta x) for the mean function is presented. The new test depends on the selection of weight functions. An expression for the efficacy of the proposed test under a sequence of local alternatives will be given. With the application of this result one can direct the choice of the optimal weight functions in order to maximize the efficacy. The new test is simple in computation and consistent against a broad class of alternatives. Asymptotically, the null distribution is independent of the underlying distribution of Y given X = x. Two pratical examples are given to illustrate the method. Further, simulation studies are given to show the advantages of the proposed test.
Relation:	STATISTICA SINICA
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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