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

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