Estimation of exponential regerssion parameters using binary data

機構典藏 > College of Business and Management > Graduate Institute & Department of Statistics > Journal Article > Item 987654321/69190

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/69190

Title:	Estimation of exponential regerssion parameters using binary data
Authors:	Cheng, K.F.;Wu, Jong-wuu
Contributors:	淡江大學統計學系
Keywords:	Exponential regression;least square method;binary data;maximum likelihood estimate
Date:	1992-08
Issue Date:	2013-05-31 11:38:36 (UTC+8)
Publisher:	Philadelphia: Taylor & Francis Inc.
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 ellipticaily symmetric distributions, for the explanatory variables, the estimators are 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 are 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 21(8), pp.2203-2214
DOI:	10.1080/03610929208830907
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	163	View/Open