JSM 2005 - Toronto

Abstract #303616

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 190
Type: Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #303616
Title: Estimation of Missing Values Using Multivariate Normal Copula
Author(s): Rahul Parsa*+ and Alain Desgagne
Companies: Drake University and Drake University
Address: 2507 University Av, Des Moines, IA, 50311, United States
Keywords: Missing Values ; EM Algorithm ; Multivariate Normal Copula ; Maximum Likelihood
Abstract:

Datasets with missing observations are a common occurrence. In the past, such datasets were analyzed by deleting the missing cases and analyzing only the complete cases. Recently, statisticians have developed several methods to replace these missing values, including multiple imputations, data augmentation, Expectation and Maximization (EM) algorithm, and multiple linear regression. Little and Rubin (1987) were one of the significant contributions to these areas. Most of the current imputation methods assume the joint distribution of the variables to be multivariate normal. This assumption may not be appropriate for all datasets. The purpose of this paper is to show how copulas can be used to estimate the missing values without the assumption of multivariate normal distribution and provide the researcher the opportunity to model marginal distributions with nonnormal distributions. In particular, we will use multivariate normal copula to model the joint multivariate distribution. We will use the EM algorithm in conjunction with Inference Functions for Margins (IFM) (Joe 1997) method to estimate the parameters and missing values.

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Revised March 2005