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Abstract Details

Activity Number: 658
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #306335
Title: Conditional-Mean Model Selection with Multiple Covariates and Missing Data
Author(s): Gregory DiRienzo*+
Address: University at Albany - SUNY, Rensselaer, NY, 12144, United States
Keywords: Bootstrap ; Cross-Validation ; Generalized linear model ; Mis-specified model ; Multiple hypothesis testing ; Multiple hypothesis testing

An objective methodology to select a parsimonious conditional-mean model from multiple covariates and missing data is proposed. The methods assume data is missing at random and employ inverse probability weighted complete case estimation of regression coefficients and cross-validated prediction error for working models. Using these estimates and an objective model comparison procedure, the selection proceeds in a forward manner and is controlled by the pre-selected maximum number of covariate terms allowed in the final model. Simultaneous hypothesis testing procedures are used to attempt to asymptotically control the number of unimportant covariates in the final model choice. The methods are evaluated using a simulation study and are applied to a dataset of birth certificate records and infant mortality in the United States.

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