Paper Info
Title
Mixtures of common t-factor analyzers for modeling high-dimensional data with missing values.
Abstract
Mixtures of common t-factor analyzers (MCtFA) have emerged as a sound parsimonious model-based tool for robust modeling of high-dimensional data in the presence of fat-tailed noises and atypical observations. This paper presents a generalization of MCtFA to accommodate missing values as they frequently occur in many scientific researches. Under a missing at random mechanism, a computationally efficient Expectation Conditional Maximization Either (ECME) algorithm is developed for parameter estimation. The techniques for visualization of the data, classification of new individuals, and imputation of missing values under an incomplete-data structure of MCtFA are also investigated. Illustrative examples concerning the analysis of real and simulated data sets are presented to describe the usefulness of the proposed methodology and compare the finite sample performance with its normal counterparts.
Year
DOI
Venue
2015
10.1016/j.csda.2014.10.007
Computational Statistics & Data Analysis
Keywords
Field
DocType
Clustering,Common factor loadings,Dimension reduction,ECME algorithm,Missing data,Visualization
Econometrics,Data mining,Data set,Clustering high-dimensional data,Dimensionality reduction,Computer science,Imputation (statistics),Missing data,Estimation theory,Cluster analysis,Statistics,Maximization
Journal
Volume
ISSN
Citations 
83
0167-9473
4
PageRank 
References 
Authors
0.44
23
1
Name
Order
Citations
PageRank
Wan-Lun Wang1476.48