Learning from incomplete data via parameterized <mml:math altimg="si29.gif" display="inline" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd" xmlns:sa="http://www.elsevier.com/xml/common/struct-aff/dtd"><mml:mi>t</mml:mi></mml:math> mixture models through eigenvalue decomposition

Using general regression with local tuning for learning mixture models from incomplete data sets

Egyptian Informatics Journal ◽

10.1016/j.eij.2010.10.001 ◽

2010 ◽

Vol 11 (2) ◽

pp. 49-57 ◽

Cited By ~ 6

Author(s):

Ahmed R. Abas

Keyword(s):

Mixture Models ◽

Incomplete Data ◽

Data Sets ◽

Local Tuning ◽

General Regression

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Mixture Models for Learning from Incomplete Data

Computational Learning Theory and Natural Learning Systems ◽

10.7551/mitpress/2016.003.0010 ◽

1997 ◽

Keyword(s):

Mixture Models ◽

Incomplete Data

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Selection Models and Pattern-Mixture Models for Incomplete Data with Covariates

Biometrics ◽

10.1111/j.0006-341x.1999.00978.x ◽

1999 ◽

Vol 55 (3) ◽

pp. 978-983 ◽

Cited By ~ 21

Author(s):

Bart Michiels ◽

Geert Molenberghs ◽

Stuart R. Lipsitz

Keyword(s):

Mixture Models ◽

Incomplete Data ◽

Selection Models

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Pattern-Mixture Models for Multivariate Incomplete Data

Journal of the American Statistical Association ◽

10.2307/2290705 ◽

1993 ◽

Vol 88 (421) ◽

pp. 125 ◽

Cited By ~ 149

Author(s):

Roderick J. A. Little

Keyword(s):

Mixture Models ◽

Incomplete Data

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A Note on MAR, Identifying Restrictions, Model Comparison, and Sensitivity Analysis in Pattern Mixture Models with and without Covariates for Incomplete Data

Biometrics ◽

10.1111/j.1541-0420.2011.01565.x ◽

2011 ◽

Vol 67 (3) ◽

pp. 810-818 ◽

Cited By ~ 24

Author(s):

Chenguang Wang ◽

Michael J. Daniels

Keyword(s):

Sensitivity Analysis ◽

Mixture Models ◽

Incomplete Data ◽

Model Comparison

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Correction to “A Note on MAR, Identifying Restrictions, Model Comparison, and Sensitivity Analysis in Pattern Mixture Models with and without Covariates for Incomplete Data,” by Chenguang Wang and Michael J. Daniels; 67, 810-818, September 2011

Biometrics ◽

10.1111/j.1541-0420.2012.01801.x ◽

2012 ◽

Vol 68 (3) ◽

pp. 994-994 ◽

Cited By ~ 2

Keyword(s):

Sensitivity Analysis ◽

Mixture Models ◽

Incomplete Data ◽

Model Comparison

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Using incremental general regression neural network for learning mixture models from incomplete data

Egyptian Informatics Journal ◽

10.1016/j.eij.2011.07.001 ◽

2011 ◽

Vol 12 (3) ◽

pp. 185-196 ◽

Cited By ~ 5

Author(s):

Ahmed R. Abas

Keyword(s):

Neural Network ◽

Mixture Models ◽

Incomplete Data ◽

General Regression Neural Network ◽

General Regression

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Pseudo-Likelihood for Combined Selection and Pattern-Mixture Models for Incomplete Data

Biometrical Journal ◽

10.1002/(sici)1521-4036(199809)40:5<557::aid-bimj557>3.0.co;2-s ◽

1998 ◽

Vol 40 (5) ◽

pp. 557-572 ◽

Cited By ~ 6

Author(s):

Geert Molenberghs ◽

Bart Michiels ◽

Michael G. Kenward

Keyword(s):

Mixture Models ◽

Incomplete Data ◽

Pseudo Likelihood ◽

Combined Selection

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Locally Tuned General Regression for Learning Mixture Models Using Small Incomplete Data Sets with Outliers and Overlapping Classes

Lecture Notes in Computer Science - Intelligent Data Engineering and Automated Learning – IDEAL 2004 ◽

10.1007/978-3-540-28651-6_115 ◽

2004 ◽

pp. 774-779 ◽

Cited By ~ 1

Author(s):

Ahmed Rafat

Keyword(s):

Mixture Models ◽

Incomplete Data ◽

Data Sets ◽

General Regression ◽

Overlapping Classes

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Bayesian estimation and classification with incomplete data using mixture models

2004 International Conference on Machine Learning and Applications, 2004. Proceedings. ◽

10.1109/icmla.2004.1383527 ◽

2005 ◽

Cited By ~ 1

Author(s):

Jufen Zhang ◽

R. Everson

Keyword(s):

Bayesian Estimation ◽

Mixture Models ◽

Incomplete Data

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Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition

Using general regression with local tuning for learning mixture models from incomplete data sets

Mixture Models for Learning from Incomplete Data

Selection Models and Pattern-Mixture Models for Incomplete Data with Covariates

Pattern-Mixture Models for Multivariate Incomplete Data

A Note on MAR, Identifying Restrictions, Model Comparison, and Sensitivity Analysis in Pattern Mixture Models with and without Covariates for Incomplete Data

Correction to “A Note on MAR, Identifying Restrictions, Model Comparison, and Sensitivity Analysis in Pattern Mixture Models with and without Covariates for Incomplete Data,” by Chenguang Wang and Michael J. Daniels; 67, 810-818, September 2011

Using incremental general regression neural network for learning mixture models from incomplete data

Pseudo-Likelihood for Combined Selection and Pattern-Mixture Models for Incomplete Data

Locally Tuned General Regression for Learning Mixture Models Using Small Incomplete Data Sets with Outliers and Overlapping Classes

Bayesian estimation and classification with incomplete data using mixture models

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