Robust Estimation of the Mean and Covariance Matrix from Data with Missing Values

Roderick J. A. Little

Journal of the Royal Statistical Society Series C, 1988, vol. 37, issue 1, 23-38

Abstract: Methods of Rubin (1983) for robust estimation of a mean and covariance matrix and associated parameters are extended to analyse data with missing values. The methods are maximum likelihood (ML) for multivariate t and contaminated normal models. ML estimation is achieved by the EM algorithm, and involves minor modifications to the EM algorithm for multivariate normal data. The methods are shown to be superior to existing methods in a simulation study, using data generated from a variety of models. Model selection and standard error estimation are discussed with the aid of two real data examples.

Date: 1988
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