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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Vydané v:Biometrics Ročník 70; číslo 2; s. 312 - 322
Hlavní autori: Chen, Lin S, Prentice, Ross L, Wang, Pei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Blackwell Publishers 01.06.2014
Blackwell Publishing Ltd
International Biometric Society
Predmet:
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
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Shrnutí: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.
Bibliografia:http://dx.doi.org/10.1111/biom.12149
ArticleID:BIOM12149
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.12149