A Comparison of Several Algorithms and Models for Analyzing Multivariate Normal Data with Missing Responses
In this paper we compare some modern algorithms i.e. Direct Maximization of the Likelihood (DML), the EM algorithm, and Multiple Imputation (MI) for analyzing multivariate normal data with missing responses. We also compare two approaches for modeling incomplete data (1) ignoring missing data and (2...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.563.9445 2023-05-15T16:01:50+02:00 A Comparison of Several Algorithms and Models for Analyzing Multivariate Normal Data with Missing Responses Mojtaba Ganjali H. Ranji The Pennsylvania State University CiteSeerX Archives 2007 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.563.9445 http://www.pvamu.edu/Include/Math/AAM/Vol3_No1/Ganjali AAM-R48-MG-071707 Final _5_ 6-12-08.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.563.9445 http://www.pvamu.edu/Include/Math/AAM/Vol3_No1/Ganjali AAM-R48-MG-071707 Final _5_ 6-12-08.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.pvamu.edu/Include/Math/AAM/Vol3_No1/Ganjali AAM-R48-MG-071707 Final _5_ 6-12-08.pdf Maximum Likelihood Data Augmentation The EM Algorithm Multiple imputations Heckman’s selection model text 2007 ftciteseerx 2016-01-08T12:10:32Z In this paper we compare some modern algorithms i.e. Direct Maximization of the Likelihood (DML), the EM algorithm, and Multiple Imputation (MI) for analyzing multivariate normal data with missing responses. We also compare two approaches for modeling incomplete data (1) ignoring missing data and (2) joint modeling of response and non-response mechanisms. Several types of Software which can be used to implement the above algorithms are also mentioned. We used these algorithms for a simulation study and to analyze a data set where outliers affect the parameter estimates and final conclusion. As the variance of the estimates cannot be obtained using the available software for some of the algorithms, a bootstrap method is used to find them. Text DML Unknown |
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language |
English |
topic |
Maximum Likelihood Data Augmentation The EM Algorithm Multiple imputations Heckman’s selection model |
spellingShingle |
Maximum Likelihood Data Augmentation The EM Algorithm Multiple imputations Heckman’s selection model Mojtaba Ganjali H. Ranji A Comparison of Several Algorithms and Models for Analyzing Multivariate Normal Data with Missing Responses |
topic_facet |
Maximum Likelihood Data Augmentation The EM Algorithm Multiple imputations Heckman’s selection model |
description |
In this paper we compare some modern algorithms i.e. Direct Maximization of the Likelihood (DML), the EM algorithm, and Multiple Imputation (MI) for analyzing multivariate normal data with missing responses. We also compare two approaches for modeling incomplete data (1) ignoring missing data and (2) joint modeling of response and non-response mechanisms. Several types of Software which can be used to implement the above algorithms are also mentioned. We used these algorithms for a simulation study and to analyze a data set where outliers affect the parameter estimates and final conclusion. As the variance of the estimates cannot be obtained using the available software for some of the algorithms, a bootstrap method is used to find them. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Mojtaba Ganjali H. Ranji |
author_facet |
Mojtaba Ganjali H. Ranji |
author_sort |
Mojtaba Ganjali |
title |
A Comparison of Several Algorithms and Models for Analyzing Multivariate Normal Data with Missing Responses |
title_short |
A Comparison of Several Algorithms and Models for Analyzing Multivariate Normal Data with Missing Responses |
title_full |
A Comparison of Several Algorithms and Models for Analyzing Multivariate Normal Data with Missing Responses |
title_fullStr |
A Comparison of Several Algorithms and Models for Analyzing Multivariate Normal Data with Missing Responses |
title_full_unstemmed |
A Comparison of Several Algorithms and Models for Analyzing Multivariate Normal Data with Missing Responses |
title_sort |
comparison of several algorithms and models for analyzing multivariate normal data with missing responses |
publishDate |
2007 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.563.9445 http://www.pvamu.edu/Include/Math/AAM/Vol3_No1/Ganjali AAM-R48-MG-071707 Final _5_ 6-12-08.pdf |
genre |
DML |
genre_facet |
DML |
op_source |
http://www.pvamu.edu/Include/Math/AAM/Vol3_No1/Ganjali AAM-R48-MG-071707 Final _5_ 6-12-08.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.563.9445 http://www.pvamu.edu/Include/Math/AAM/Vol3_No1/Ganjali AAM-R48-MG-071707 Final _5_ 6-12-08.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766397548092719104 |