Bayesian Markov chain Monte Carlo imputation for the transiting exoplanets with an application in clustering analysis

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/109162

Title:	Bayesian Markov chain Monte Carlo imputation for the transiting exoplanets with an application in clustering analysis
Authors:	洪文良;Teng, Huei-Wen;Hung, Wen-Liang;Chao, Yen-Ju
Contributors:	理學院數學系
Keywords:	Bayesian analysis;Cluster analysis;Clustering;Clusters;copula;Evolution;Extrasolar planets;Formations;hot Jupiters;Markov analysis;Markov chains;Metropolis-Hastings algorithm;missing data;Monte Carlo methods;Monte Carlo simulation;Pareto optimum;Studies;transiting exoplanets
Date:	2015-05-04
Issue Date:	2026-04-23 16:11:22 (UTC+8)
Publisher:	Routledge;Abingdon: Taylor & Francis
Abstract:	摘要： To impute the missing values of mass in the transiting exoplanet data, this paper uses the Frank copula to combine two Pareto marginal distributions. Next, a Bayesian Markov chain Monte Carlo (MCMC) imputation method is proposed. The proposed Bayesian MCMC imputation method is found to outperform the mean imputation method. Clustering analysis can shed light on the formation and evolution of exoplanets. After imputing the missing values of mass in the transiting exoplanet data using the proposed approach, the similarity-based clustering method (SCM) clustering algorithm is applied to the logarithm of mass and period for this complete data set. The SCM clustering result indicates two clusters. Furthermore, the intracluster Spearman rank-order correlation coefficients for mass and period in these two clusters are 0.401 and , respectively, at a significance level of 0.01. This result illustrates that the mass and period correlate in an opposite way between the two different clusters. It implies that the formation and evolution processes of these two clusters are different. 出版者： Abingdon: Taylor & Francis 出版日期： 2015-05-04 出處： Journal of applied statistics, 2015-05, Vol.42 (5), p.1120-1132 資源來源： Business Source Premier - EBSCO 版權： 2015 Taylor & Francis 2015 版權： Copyright Taylor & Francis Ltd. 2015 識別號： ISSN: 0266-4763 識別號： EISSN: 1360-0532 識別號： DOI: 10.1080/02664763.2014.995609
Appears in Collections:	[Department of Mathematics] journal & Dissertation

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