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Semiparametric maximum likelihood fo...

Suh, Eun-Young.

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Semiparametric maximum likelihood for regression with measurement error.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Semiparametric maximum likelihood for regression with measurement error./
作者:	Suh, Eun-Young.
面頁冊數:	101 p.
附註:	Adviser: Daniel W. Schafer.
Contained By:	Dissertation Abstracts International62-05B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3015233
ISBN:	0493252061

Semiparametric maximum likelihood for regression with measurement error.
Suh, Eun-Young.

Semiparametric maximum likelihood for regression with measurement error. - 101 p.

Adviser: Daniel W. Schafer.

Thesis (Ph.D.)--Oregon State University, 2001.

Semiparametric maximum likelihood analysis allows inference in errors-in-variables models with small loss of efficiency relative to full likelihood analysis but with significantly weakened assumptions. In addition, since no distributional assumptions are made for the nuisance parameters, the analysis more nearly parallels that for usual regression. These highly desirable features and the high degree of modelling flexibility permitted warrant the development of the approach for routine use. This thesis does so for the special cases of linear and nonlinear regression with measurement errors in one explanatory variable. A transparent and flexible computational approach is developed, the analysis is exhibited on some examples, and finite sample properties of estimates, approximate standard errors, and likelihood ratio inference are clarified with simulation.

ISBN: 0493252061Subjects--Topical Terms:

517247
Statistics.

Semiparametric maximum likelihood for regression with measurement error.
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500 $a Source: Dissertation Abstracts International, Volume: 62-05, Section: B, page: 2372.
502 $a Thesis (Ph.D.)--Oregon State University, 2001.
520 $a Semiparametric maximum likelihood analysis allows inference in errors-in-variables models with small loss of efficiency relative to full likelihood analysis but with significantly weakened assumptions. In addition, since no distributional assumptions are made for the nuisance parameters, the analysis more nearly parallels that for usual regression. These highly desirable features and the high degree of modelling flexibility permitted warrant the development of the approach for routine use. This thesis does so for the special cases of linear and nonlinear regression with measurement errors in one explanatory variable. A transparent and flexible computational approach is developed, the analysis is exhibited on some examples, and finite sample properties of estimates, approximate standard errors, and likelihood ratio inference are clarified with simulation.
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791 $a Ph.D.
792 $a 2001
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