Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman
In most practical applications, data sets are often contaminated with error or mismeasured covariates. When these errors-in-variables or measurement errors are not corrected, they will cause misleading statistical inferences and analysis. Therefore, we will focus on addressing errors-in-variables...
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| Format: | Thesis |
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2018
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| Online Access: | http://studentsrepo.um.edu.my/9536/1/Nur_Aainaa_Rozilman.pdf http://studentsrepo.um.edu.my/9536/9/aainaa.pdf http://studentsrepo.um.edu.my/9536/ |
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| _version_ | 1831435366498828288 |
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| author | Nur Aainaa , Rozliman |
| author_facet | Nur Aainaa , Rozliman |
| author_sort | Nur Aainaa , Rozliman |
| building | UM Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaya |
| content_source | UM Student Repository |
| continent | Asia |
| country | Malaysia |
| description | In most practical applications, data sets are often contaminated with error or mismeasured
covariates. When these errors-in-variables or measurement errors are not corrected,
they will cause misleading statistical inferences and analysis. Therefore, we will focus
on addressing errors-in-variables problems in count data regression models, specifically
Poisson regression and negative binomial regression models. To remain useful in realistic
situations, we utilize the Bayesian approach where the variance is estimated instead
of assumed as known. We relax the distributional assumption of the exposure model by
intentionally misspecifying the model with a flexible distribution. Following this, we shall
also compare the performance between two different flexible distributions in modelling
the exposure, namely the flexible generalized skew-normal distribution and flexible skewgeneralized
normal distribution. We also conduct simulation studies on synthetic data sets
using Markov Chain Monte Carlo simulation techniques to investigate the performance of
the flexible Bayesian approach. The results of our findings show that the flexible Bayesian
approach is able to estimate the values of the true regression parameters consistently and
accurately with a significant bias reduction. |
| format | Thesis |
| id | my.um.stud-9536 |
| institution | Universiti Malaya |
| publishDate | 2018 |
| record_format | eprints |
| spelling | my.um.stud-95362021-05-05T19:33:27Z Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman Nur Aainaa , Rozliman Q Science (General) QA Mathematics In most practical applications, data sets are often contaminated with error or mismeasured covariates. When these errors-in-variables or measurement errors are not corrected, they will cause misleading statistical inferences and analysis. Therefore, we will focus on addressing errors-in-variables problems in count data regression models, specifically Poisson regression and negative binomial regression models. To remain useful in realistic situations, we utilize the Bayesian approach where the variance is estimated instead of assumed as known. We relax the distributional assumption of the exposure model by intentionally misspecifying the model with a flexible distribution. Following this, we shall also compare the performance between two different flexible distributions in modelling the exposure, namely the flexible generalized skew-normal distribution and flexible skewgeneralized normal distribution. We also conduct simulation studies on synthetic data sets using Markov Chain Monte Carlo simulation techniques to investigate the performance of the flexible Bayesian approach. The results of our findings show that the flexible Bayesian approach is able to estimate the values of the true regression parameters consistently and accurately with a significant bias reduction. 2018-09 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/9536/1/Nur_Aainaa_Rozilman.pdf application/pdf http://studentsrepo.um.edu.my/9536/9/aainaa.pdf Nur Aainaa , Rozliman (2018) Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman. Masters thesis, University of Malaya. http://studentsrepo.um.edu.my/9536/ |
| spellingShingle | Q Science (General) QA Mathematics Nur Aainaa , Rozliman Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman |
| title | Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman |
| title_full | Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman |
| title_fullStr | Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman |
| title_full_unstemmed | Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman |
| title_short | Bayesian approach to errors-in-variables in count data regression models / Nur Aainaa Rozliman |
| title_sort | bayesian approach to errors-in-variables in count data regression models / nur aainaa rozliman |
| topic | Q Science (General) QA Mathematics |
| url | http://studentsrepo.um.edu.my/9536/1/Nur_Aainaa_Rozilman.pdf http://studentsrepo.um.edu.my/9536/9/aainaa.pdf http://studentsrepo.um.edu.my/9536/ |
| url_provider | http://studentsrepo.um.edu.my/ |