penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation

• Published in: Biometrics Vol. 70; no. 2; pp. 312 - 322
• Main Authors: Chen, Lin S, Prentice, Ross L, Wang, Pei
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishers 01.06.2014; Blackwell Publishing Ltd; International Biometric Society
• Subjects: Algorithms; Analytical estimating; BIOMETRIC METHODOLOGY; Biometrics; biometry; Biometry - methods; Computer Simulation; Covariance; Covariance matrices; Data imputation; Estimating techniques; Estimation methods; Expectation-maximization (EM) algorithm; Fungal Proteins - analysis; Humans; Likelihood Functions; Mass Spectrometry; Maximum penalized likelihood estimate; Missing data; Models, Statistical; Multivariate Analysis; Normal Distribution; Not-missing-at-random (NMAR); Parameter estimation; Proteomics; Proteomics - statistics & numerical data; Sample size; Statistical variance; Not-missing-at-random (NMAR); Maximum penalized likelihood estimate; Expectation-maximization (EM) algorithm
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12149

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.