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A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation
Author(s) -
Chen Lin S.,
Prentice Ross L.,
Wang Pei
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12149
Subject(s) - missing data , covariance , algorithm , gaussian , computer science , multivariate normal distribution , multivariate statistics , expectation–maximization algorithm , estimation theory , data mining , mathematics , statistics , maximum likelihood , physics , quantum mechanics
Summary 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.