Outlier detection in 2 × 2 crossover design using Bayesian framework
We consider the problem of outlier detection method in 2×2 crossover design via Bayesian framework. We study the problem of outlier detection in bivariate data fitted using generalized linear model in Bayesian framework used by Nawama. We adapt their work into a 2×2 crossover design. In Bayesian fra...
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Penerbit Universiti Kebangsaan Malaysia
2019
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my.upm.eprints.693102020-03-25T09:13:22Z http://psasir.upm.edu.my/id/eprint/69310/ Outlier detection in 2 × 2 crossover design using Bayesian framework Lim, Fong Peng Mohamed, Ibrahim Ibrahim, Adriana Irawati Nur Goh, S. L. Mohamed @ A. Rahman, Nur Anisah We consider the problem of outlier detection method in 2×2 crossover design via Bayesian framework. We study the problem of outlier detection in bivariate data fitted using generalized linear model in Bayesian framework used by Nawama. We adapt their work into a 2×2 crossover design. In Bayesian framework, we assume that the random subject effect and the errors to be generated from normal distributions. However, the outlying subjects come from normal distribution with different variance. Due to the complexity of the resulting joint posterior distribution, we obtain the information on the posterior distribution from samples by using Markov Chain Monte Carlo sampling. We use two real data sets to illustrate the implementation of the method. Penerbit Universiti Kebangsaan Malaysia 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/69310/1/Outlier%20detection%20in%202%20%C3%97%202%20crossover%20design%20using%20Bayesian%20framework.pdf Lim, Fong Peng and Mohamed, Ibrahim and Ibrahim, Adriana Irawati Nur and Goh, S. L. and Mohamed @ A. Rahman, Nur Anisah (2019) Outlier detection in 2 × 2 crossover design using Bayesian framework. Sains Malaysiana, 48 (4). pp. 893-899. ISSN 0126-6039 http://www.ukm.my/jsm/english_journals/vol48num4_2019/contentsVol48num4_2019.html 10.17576/jsm-2019-4804-22 |
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English |
| description |
We consider the problem of outlier detection method in 2×2 crossover design via Bayesian framework. We study the problem of outlier detection in bivariate data fitted using generalized linear model in Bayesian framework used by Nawama. We adapt their work into a 2×2 crossover design. In Bayesian framework, we assume that the random subject effect and the errors to be generated from normal distributions. However, the outlying subjects come from normal distribution with different variance. Due to the complexity of the resulting joint posterior distribution, we obtain the information on the posterior distribution from samples by using Markov Chain Monte Carlo sampling. We use two real data sets to illustrate the implementation of the method. |
| format |
Article |
| author |
Lim, Fong Peng Mohamed, Ibrahim Ibrahim, Adriana Irawati Nur Goh, S. L. Mohamed @ A. Rahman, Nur Anisah |
| spellingShingle |
Lim, Fong Peng Mohamed, Ibrahim Ibrahim, Adriana Irawati Nur Goh, S. L. Mohamed @ A. Rahman, Nur Anisah Outlier detection in 2 × 2 crossover design using Bayesian framework |
| author_facet |
Lim, Fong Peng Mohamed, Ibrahim Ibrahim, Adriana Irawati Nur Goh, S. L. Mohamed @ A. Rahman, Nur Anisah |
| author_sort |
Lim, Fong Peng |
| title |
Outlier detection in 2 × 2 crossover design using Bayesian framework |
| title_short |
Outlier detection in 2 × 2 crossover design using Bayesian framework |
| title_full |
Outlier detection in 2 × 2 crossover design using Bayesian framework |
| title_fullStr |
Outlier detection in 2 × 2 crossover design using Bayesian framework |
| title_full_unstemmed |
Outlier detection in 2 × 2 crossover design using Bayesian framework |
| title_sort |
outlier detection in 2 × 2 crossover design using bayesian framework |
| publisher |
Penerbit Universiti Kebangsaan Malaysia |
| publishDate |
2019 |
| url |
http://psasir.upm.edu.my/id/eprint/69310/1/Outlier%20detection%20in%202%20%C3%97%202%20crossover%20design%20using%20Bayesian%20framework.pdf http://psasir.upm.edu.my/id/eprint/69310/ http://www.ukm.my/jsm/english_journals/vol48num4_2019/contentsVol48num4_2019.html |
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1665895988968030208 |
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13.251813 |