dc.contributor.author	Starnes, Brett Alden	en
dc.contributor.committeechair	Birch, Jeffrey B.	en
dc.contributor.committeemember	Anderson-Cook, Christine M.	en
dc.contributor.committeemember	Smith, Eric P.	en
dc.contributor.committeemember	Coakley, Clint W.	en
dc.contributor.committeemember	Terrell, George R.	en
dc.contributor.department	Statistics	en
dc.date.accessioned	2014-03-14T20:21:07Z	en
dc.date.adate	1999-12-31	en
dc.date.available	2014-03-14T20:21:07Z	en
dc.date.issued	1999-12-14	en
dc.date.rdate	2004-04-11	en
dc.date.sdate	1999-12-22	en
dc.description.abstract	Since the mid 1980's many statisticians have studied methods for combining parametric and nonparametric esimates to improve the quality of fits in a regression problem. Notably in 1987, Einsporn and Birch proposed the Model Robust Regression estimate (MRR1) in which estimates of the parametric function, ƒ, and the nonparametric function, 𝑔, were combined in a straightforward fashion via the use of a mixing parameter, λ. This technique was studied extensively at small samples and was shown to be quite effective at modeling various unusual functions. In 1995, Mays and Birch developed the MRR2 estimate as an alternative to MRR1. This model involved first forming the parametric fit to the data, and then adding in an estimate of 𝑔 according to the lack of fit demonstrated by the error terms. Using small samples, they illustrated the superiority of MRR2 to MRR1 in most situations. In this dissertation we have developed asymptotic convergence rates for both MRR1 and MRR2 in OLS and GLS (maximum likelihood) settings. In many of these settings, it is demonstrated that the user of MRR1 or MRR2 achieves the best convergence rates available regardless of whether or not the model is properly specified. This is the "Golden Result of Model Robust Regression". It turns out that the selection of the mixing parameter is paramount in determining whether or not this result is attained.	en
dc.description.degree	Ph. D.	en
dc.identifier.other	etd-122299-184429	en
dc.identifier.sourceurl	http://scholar.lib.vt.edu/theses/available/etd-122299-184429/	en
dc.identifier.uri	http://hdl.handle.net/10919/30244	en
dc.publisher	Virginia Tech	en
dc.relation.haspart	S7994Ch4b.PDF	en
dc.relation.haspart	S7994x4a.PDF	en
dc.relation.haspart	S7994Ch6.PDF	en
dc.relation.haspart	S7994rb.pdf	en
dc.relation.haspart	S7994x3a.PDF	en
dc.relation.haspart	S7994x3b.PDF	en
dc.relation.haspart	S7994x3c.PDF	en
dc.relation.haspart	S7994Ch4a.PDF	en
dc.relation.haspart	S7994Ch3c.PDF	en
dc.relation.haspart	S7994Vita.PDF	en
dc.relation.haspart	S7994x5b.PDF	en
dc.relation.haspart	S7994x5a.PDF	en
dc.relation.haspart	S7994Ch3b.PDF	en
dc.relation.haspart	S7994x4b.PDF	en
dc.relation.haspart	S7994Ch5b.PDF	en
dc.relation.haspart	S7994Ch3a.PDF	en
dc.relation.haspart	S7994Ch2b.PDF	en
dc.relation.haspart	S7994Ch2a.PDF	en
dc.relation.haspart	S7994Ch1.PDF	en
dc.relation.haspart	S7994frontmatter2.PDF	en
dc.relation.haspart	S7994Ch2c.PDF	en
dc.relation.haspart	S7994frontmatter.PDF	en
dc.relation.haspart	S7994Ch5a.PDF	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Mixing Parameter	en
dc.subject	Nonparametric	en
dc.subject	Parametric	en
dc.subject	Asymptotic	en
dc.subject	Convergence Rates	en
dc.subject	Regression	en
dc.subject	Semiparametric	en
dc.title	Asymptotic Results for 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

Asymptotic Results for Model Robust Regression

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