ESTIMATION OF EXPONENTIAL REGRESSION PARAMETERS USING BINARY DATA

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27845

Title:	ESTIMATION OF EXPONENTIAL REGRESSION PARAMETERS USING BINARY DATA
Authors:	CHENG,KF;WU,JW
Contributors:	統計研究所
Date:	1992
Issue Date:	2010-06-29 19:34:44 (UTC+8)
Publisher:	中央大學
Abstract:	Exponential regression model is important in analyzing data from heterogeneous populations. In this paper we propose a simple method to estimate the regression parameters using binary data. Under certain design distributions, including elliptically symmetric distributions, for the explanatory variables, the estimators axe shown to be consistent and asymptotically normal when sample size is large. For finite samples, the new estimates were shown to behave reasonably well. They axe competitive with the maximum likelihood estimates and more importantly, according to our simulation results, the cost of CPU time for computing new estimates is only 1/7 of that required for computing the usual maximum likelihood estimates. We expect the savings in CPU time would be more dramatic with larger dimension of the regression parameter space.
Relation:	COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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