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Model selection for unbiased estimat...

Liu, Xiangyang.

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Model selection for unbiased estimating equations.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Model selection for unbiased estimating equations./
作者:	Liu, Xiangyang.
面頁冊數:	152 p.
附註:	Adviser: Martin A. Tanner.
Contained By:	Dissertation Abstracts International61-11B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9994691
ISBN:	0493021485

Model selection for unbiased estimating equations.
Liu, Xiangyang.

Model selection for unbiased estimating equations. - 152 p.

Adviser: Martin A. Tanner.

Thesis (Ph.D.)--Northwestern University, 2000.

We consider model selection based on estimators that are asymptotically normal. Such a method can be applied to the context of estimating equations, since a full specification of the probability model or likelihood function is not required. We construct a class of “cost” functions for the models in consideration, and show that the minimizer of the cost function is a consistent estimator of the model under suitable conditions. One special cost function is closely related to the Bayes factor and Schwarz's Bayesian Information Criterion (BIC). We also show that by ranking the Z-statistics, the scope of model searching can be greatly reduced. The proposed methodology can be applied to both linear and some nonlinear models. The convergence rate of the newly proposed model selection criteria is explored as well. In addition to the simulation results, we also provide data analysis examples, and illustrate the use of these variable selection techniques in the contexts of linear regressions, partial likelihood analysis and the generalized estimating equations.

ISBN: 0493021485Subjects--Topical Terms:

517247
Statistics.

Model selection for unbiased estimating equations.
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502 $a Thesis (Ph.D.)--Northwestern University, 2000.
520 $a We consider model selection based on estimators that are asymptotically normal. Such a method can be applied to the context of estimating equations, since a full specification of the probability model or likelihood function is not required. We construct a class of “cost” functions for the models in consideration, and show that the minimizer of the cost function is a consistent estimator of the model under suitable conditions. One special cost function is closely related to the Bayes factor and Schwarz's Bayesian Information Criterion (BIC). We also show that by ranking the Z-statistics, the scope of model searching can be greatly reduced. The proposed methodology can be applied to both linear and some nonlinear models. The convergence rate of the newly proposed model selection criteria is explored as well. In addition to the simulation results, we also provide data analysis examples, and illustrate the use of these variable selection techniques in the contexts of linear regressions, partial likelihood analysis and the generalized estimating equations.
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