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 dimensi...

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Veröffentlicht in:	Biometrics Jg. 70; H. 2; S. 312 - 322
Hauptverfasser:	Chen, Lin S, Prentice, Ross L, Wang, Pei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Blackwell Publishers 01.06.2014 Blackwell Publishing Ltd International Biometric Society
Schlagworte:	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
Online-Zugang:	Volltext
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