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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Main Authors: Lim, F.P., I.B. Mohamed, A.I.N. Ibrahim, Goh, S.L., N.A. Mohamed @ A. Rahman
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2019
Online Access:http://journalarticle.ukm.my/13393/
http://journalarticle.ukm.my/13393/1/22%20F.P.%20Lim.pdf
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author Lim, F.P.
I.B. Mohamed,
A.I.N. Ibrahim,
Goh, S.L.
N.A. Mohamed @ A. Rahman,
author_facet Lim, F.P.
I.B. Mohamed,
A.I.N. Ibrahim,
Goh, S.L.
N.A. Mohamed @ A. Rahman,
author_sort Lim, F.P.
building UKM Institutional Repository
collection Online Access
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.
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publishDate 2019
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spelling oai:generic.eprints.org:133932019-09-20T23:05:28Z http://journalarticle.ukm.my/13393/ Outlier detection in 2 × 2 crossover design using Bayesian framework Lim, F.P. I.B. Mohamed, A.I.N. Ibrahim, Goh, S.L. N.A. Mohamed @ A. Rahman, 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 2019-04 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/13393/1/22%20F.P.%20Lim.pdf Lim, F.P. and I.B. Mohamed, and A.I.N. Ibrahim, and Goh, S.L. and N.A. Mohamed @ A. Rahman, (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/malay_journals/jilid48bil4_2019/KandunganJilid48Bil4_2019.html
spellingShingle Lim, F.P.
I.B. Mohamed,
A.I.N. Ibrahim,
Goh, S.L.
N.A. Mohamed @ A. Rahman,
Outlier detection in 2 × 2 crossover design using Bayesian framework
title 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_short Outlier detection in 2 × 2 crossover design using Bayesian framework
title_sort outlier detection in 2 × 2 crossover design using bayesian framework
url http://journalarticle.ukm.my/13393/
http://journalarticle.ukm.my/13393/
http://journalarticle.ukm.my/13393/1/22%20F.P.%20Lim.pdf