A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation. Issue 2 (28th January 2014)

• Record Type: Journal Article
• Authors: Chen, Lin S.; Prentice, Ross L.; Wang, Pei
• Is Part Of: Biometrics. Volume 70:Issue 2(2014)
• Page Start: 312
• Page End: 322
• Publication Date: 2014-01-28
• Subjects: Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12149
• Languages: English
• ISSNs: 0006-341X

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Summary</title> <sec id="biom12149-sec-0001" sec-type="section"> <p>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 (<italic>p</italic>) of the response vector is equal to or greater than the number (<italic>n</italic>) 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 (<monospace>PEMM</monospace>)" estimation procedure is evaluated in simulation studies and in a proteomic data illustration.</p> </sec> </abstract>