Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis
In this article, we propose a family of bounded influence robust estimates for the parametric and non-parametric components of a generalized partially linear mixed model that are subject to censored responses and missing covariates. The asymptotic properties of the proposed estimates have been looke...
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Online Access: | http://hdl.handle.net/10.1080/02664763.2014.910886 |
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ftrepec:oai:RePEc:taf:japsta:v:41:y:2014:i:11:p:2418-2436 2023-05-15T15:01:05+02:00 Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis Kalyan Das Angshuman Sarkar http://hdl.handle.net/10.1080/02664763.2014.910886 unknown http://hdl.handle.net/10.1080/02664763.2014.910886 article ftrepec 2020-12-04T13:32:30Z In this article, we propose a family of bounded influence robust estimates for the parametric and non-parametric components of a generalized partially linear mixed model that are subject to censored responses and missing covariates. The asymptotic properties of the proposed estimates have been looked into. The estimates are obtained by using Monte Carlo expectation--maximization algorithm. An approximate method which reduces the computational time to a great extent is also proposed. A simulation study shows that performances of the two approaches are similar in terms of bias and mean square error. The analysis is illustrated through a study on the effect of environmental factors on the phytoplankton cell count. Article in Journal/Newspaper Arctic Phytoplankton RePEc (Research Papers in Economics) Arctic |
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Open Polar |
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RePEc (Research Papers in Economics) |
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description |
In this article, we propose a family of bounded influence robust estimates for the parametric and non-parametric components of a generalized partially linear mixed model that are subject to censored responses and missing covariates. The asymptotic properties of the proposed estimates have been looked into. The estimates are obtained by using Monte Carlo expectation--maximization algorithm. An approximate method which reduces the computational time to a great extent is also proposed. A simulation study shows that performances of the two approaches are similar in terms of bias and mean square error. The analysis is illustrated through a study on the effect of environmental factors on the phytoplankton cell count. |
format |
Article in Journal/Newspaper |
author |
Kalyan Das Angshuman Sarkar |
spellingShingle |
Kalyan Das Angshuman Sarkar Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis |
author_facet |
Kalyan Das Angshuman Sarkar |
author_sort |
Kalyan Das |
title |
Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis |
title_short |
Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis |
title_full |
Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis |
title_fullStr |
Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis |
title_full_unstemmed |
Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis |
title_sort |
robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to arctic data analysis |
url |
http://hdl.handle.net/10.1080/02664763.2014.910886 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Phytoplankton |
genre_facet |
Arctic Phytoplankton |
op_relation |
http://hdl.handle.net/10.1080/02664763.2014.910886 |
_version_ |
1766333132377686016 |