JSM 2004 - Toronto

Abstract #301610

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Activity Number: 150
Type: Topic Contributed
Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #301610
Title: Session on MI: Applications and Comparisons
Author(s): Wei Chen*+ and Trivellore E. Raghunathan
Companies: University of Michigan and University of Michigan
Address: 611 Church St., Rm. 244, Ann Arbor, MI, 48104,
Keywords: multiple imputation ; spline smoothing ; generalized semiparametric regression ; Gibbs sampler
Abstract:

Sequential regression multiple imputation (SRMI) is a procedure to impute missing values for a relatively complex multivariate data structure when the data are missing at random. Fitting a sequence of regression models and drawing values from the corresponding posterior predictive distributions (ppd) will obtain the imputed values. This paper is to extend SRMI by relaxing the parametric regression assumption and using spline smoothing to establish the ppd. We developed an easily implemented algorithm using the Gibbs sampler to produce posterior inferences on the smoothing spline. The application of the approach is evaluated using the alcohol consumption data. A simulation study shows the proposed method is less sensitive to the sampling properties of the imputed data than the current method in SRMI.

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