ASASProgram CombiningRFunctionalities to Implement Pattern-Mixture Models

Strategies to fit pattern-mixture models.

Whereas most models for incomplete longitudinal data are formulated within the selection model framework, pattern-mixture models have gained considerable interest in recent years (Little, 1993, 1994). In this paper, we outline several strategies to fit pattern-mixture models, including the so-called identifying restrictions strategy. Multiple imputation is used to apply this strategy to realist...

Selection and pattern-mixture models

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Pattern Recognition with Gaussian Mixture Models of Marginal Distributions

Precise estimation of data distribution with a small number of sample patterns is an important and challenging problem in the field of statistical pattern recognition. In this paper, we propose a novel method for estimating multimodal data distribution based on the Gaussian mixture model. In the proposed method, multiple random vectors are generated after classifying the elements of the feature...

Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures

Methods for dealing with missing data in clinical trials have been receiving increasing attention from the regulators, practitioners and academicians in the pharmaceutical industry over the past years. New guidelines and recommendations place a great emphasis not only on the importance of carefully selecting primary analysis methods based on clearly formulated assumptions regarding the missingn...

An Introduction to mixture models