A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation.
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.
Department of Health Studies, University of Chicago, 5841 S Maryland Ave, Chicago, Illinois, U.S.A.
Lin S
LS
Chen
Ross L
RL
Prentice
Pei
P
Wang
D000465:Algorithms; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D005656:Fungal Proteins / Q000032:analysis; D006801:Humans; D016013:Likelihood Functions; D013058:Mass Spectrometry; D015233:Models, Statistical; D015999:Multivariate Analysis; D016011:Normal Distribution; D040901:Proteomics / Q000706:statistics & numerical data*
D005656:Fungal Proteins
Expectation-maximization (EM) algorithm; Maximum penalized likelihood estimate; Not-missing-at-random (NMAR)