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Biometrics. 2014 Jun;70(2):312-22. doi: 10.1111/biom.12149. Epub 2014 Jan 28.

A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation.

Author information

1
Department of Health Studies, University of Chicago, 5841 S Maryland Ave, Chicago, Illinois, U.S.A.

Abstract

Missing data rates could depend on the targeted values in many settings, including mass spectrometry-based proteomic profiling studies. Here, we consider mean and covariance estimation under a multivariate Gaussian distribution with non-ignorable missingness, including scenarios in which the dimension (p) of the response vector is equal to or greater than the number (n) of independent observations. A parameter estimation procedure is developed by maximizing a class of penalized likelihood functions that entails explicit modeling of missing data probabilities. The performance of the resulting "penalized EM algorithm incorporating missing data mechanism (PEMM)" estimation procedure is evaluated in simulation studies and in a proteomic data illustration.

KEYWORDS:

Expectation-maximization (EM) algorithm; Maximum penalized likelihood estimate; Not-missing-at-random (NMAR)

PMID:
24471933
PMCID:
PMC4061266
DOI:
10.1111/biom.12149
[Indexed for MEDLINE]
Free PMC Article
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