dc.contributor.author	Robinson, Timothy J.	en
dc.contributor.committeechair	Birch, Jeffrey B.	en
dc.contributor.committeemember	Myers, Raymond H.	en
dc.contributor.committeemember	Smith, Eric P.	en
dc.contributor.committeemember	Terrell, George R.	en
dc.contributor.committeemember	Coakley, Clint W.	en
dc.contributor.department	Statistics	en
dc.date.accessioned	2011-08-22T19:04:32Z	en
dc.date.adate	1997-04-15	en
dc.date.available	2011-08-22T19:04:32Z	en
dc.date.issued	2004-07-30	en
dc.date.rdate	1998-04-15	en
dc.date.sdate	1998-07-25	en
dc.description.abstract	In typical normal theory regression, the assumption of homogeneity of variances is often not appropriate. Instead of treating the variances as a nuisance and transforming away the heterogeneity, the structure of the variances may be of interest and it is desirable to model the variances. Aitkin (1987) proposes a parametric dual model in which a log linear dependence of the variances on a set of explanatory variables is assumed. Aitkin's parametric approach is an iterative one providing estimates for the parameters in the mean and variance models through joint maximum likelihood. Estimation of the mean and variance parameters are interrelatedas the responses in the variance model are the squared residuals from the fit to the means model. When one or both of the models (the mean or variance model) are misspecified, parametric dual modeling can lead to faulty inferences. An alternative to parametric dual modeling is to let the data completely determine the form of the true underlying mean and variance functions (nonparametric dual modeling). However, nonparametric techniques often result in estimates which are characterized by high variability and they ignore important knowledge that the user may have regarding the process. Mays and Birch (1996) have demonstrated an effective semiparametric method in the one regressor, single-model regression setting which is a "hybrid" of parametric and nonparametric fits. Using their techniques, we develop a dual modeling approach which is robust to misspecification in either or both of the two models. Examples will be presented to illustrate the new technique, termed here as Dual Model Robust Regression.	en
dc.description.degree	Ph. D.	en
dc.format.medium	ETD	en
dc.identifier.other	etd-1322112139711101	en
dc.identifier.sourceurl	http://scholar.lib.vt.edu/theses/available/etd-1322112139711101	en
dc.identifier.uri	http://hdl.handle.net/10919/11244	en
dc.publisher	Virginia Tech	en
dc.relation.haspart	timdis.pdf	en
dc.relation.haspart	Absmic.PDF	en
dc.relation.haspart	TabContm.PDF	en
dc.relation.haspart	listfig.pdf	en
dc.relation.haspart	listtab.pdf	en
dc.relation.haspart	chap1.pdf	en
dc.relation.haspart	Chap2.pdf	en
dc.relation.haspart	chap3.pdf	en
dc.relation.haspart	chap4.pdf	en
dc.relation.haspart	chap5.pdf	en
dc.relation.haspart	chap6.pdf	en
dc.relation.haspart	chap7.pdf	en
dc.relation.haspart	chap8.pdf	en
dc.relation.haspart	chap9.pdf	en
dc.relation.haspart	appena.pdf	en
dc.relation.haspart	appenb.pdf	en
dc.relation.haspart	appenc.pdf	en
dc.relation.haspart	append.pdf	en
dc.relation.haspart	biblio.pdf	en
dc.relation.haspart	vita.pdf	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	variance estimation	en
dc.subject	nonparametric	en
dc.subject	regression	en
dc.subject	dual modeling	en
dc.title	Dual Model Robust Regression	en
dc.type	Dissertation	en
thesis.degree.discipline	Statistics	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Ph. D.	en

Dual Model Robust Regression

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