Journal ArticleDOI
Maximum likelihood estimation for mixed continuous and categorical data with missing values
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In this paper, the general location model of Olkin & Tate (1961) and extensions introduced by Krzanowski (1980, 1982) form the basis for the maximum likelihood procedures for analyzing mixed continuous and categorical data with missing values.Abstract:
SUMMARY Maximum likelihood procedures for analysing mixed continuous and categorical data with missing values are presented. The general location model of Olkin & Tate (1961) and extensions introduced by Krzanowski (1980, 1982) form the basis for our methods. Maximum likelihood estimation with incomplete data is achieved by an application of the EM algorithm (Dempster, Laird & Rubin, 1977). Special cases of the algorithm include Orchard & Woodbury's (1972) algorithm for incomplete normal samples, Fuchs's (1982) algorithms for log linear modelling of partially classified contingency tables, and Day's (1969) algorithm for multivariate normal mixtures. Applications include: (a) imputation of missing values, (b) logistic regression and discriminant analysis with missing predictors and unclassified observations, (c) linear regression with missing continuous and categorical predictors, and (d) parametric cluster analysis with incomplete data. Methods are illustrated using data from the St Louis Risk Research Project. Some key word8: Cluster analysis; Discriminant analysis; EM algorithm; Incomplete data; Linear regression; Logistic regression; Log linear model; Mixture model.read more
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MissForest—non-parametric missing value imputation for mixed-type data
TL;DR: In this comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected and the out-of-bag imputation error estimates of missForest prove to be adequate in all settings.
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A multivariate technique for multiply imputing missing values using a sequence of regression models
TL;DR: In this paper, the authors describe and evaluate a procedure for imputing missing values for a relatively complex data structure when the data are missing at random, by fitting a sequence of regression models and drawing values from corresponding predictive distributions.
Journal ArticleDOI
Modeling the Drop-Out Mechanism in Repeated-Measures Studies
TL;DR: Methods that simultaneously model the data and the drop-out process within a unified model-based framework are discussed, and possible extensions outlined.
Journal ArticleDOI
Regression with missing X’s: A review
TL;DR: The literature of regression analysis with missing values of the independent variables is reviewed in this article, where six classes of procedures are distinguished: complete case analysis, available case methods, least squares on imputed data, maximum likelihood, Bayesian methods, and multiple imputation.
Regression with Missing X's: A Review
TL;DR: Regression With Missing X's: A Review Author(s): Roderick J. A.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
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An Introduction to Multivariate Statistical Analysis
TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
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Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.
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Introduction to Multivariate Statistical Analysis.
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TL;DR: Discrete Multivariate Analysis is a comprehensive text and general reference on the analysis of discrete multivariate data, particularly in the form of multidimensional tables, and contains a wealth of material on important topics.