• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.57-68
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• 2017

Clustering algorithms attempt to find a partition of a finite set of objects in to a potentially predetermined number of nonempty subsets. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet prior distribution calculates posterior probabilities when the number of clusters was known. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. Examples are given to show how these models perform on real data.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.841-850
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• 2021

This study aims to evaluate the relationship between bank capital, efficiency, and risk in Islamic banks. We use data from 129 Islamic banks in the world, retrieved from various data sources. We retrieved specific banking data from Moody's Analytics BankFocus and Thomson Reuters Eikon, while data at the country level was obtained from the World Bank website. This study uses various estimates both Pooled OLS (Ordinary Least Square) and Random Effect (RE). However, to overcome the issue of serial correlation which could cause bias in the results of the study, we used fixed-effect (FE) cluster estimates. The research results confirm the previous findings that bank capital positively affects bank stability (natural logarithm of Z-Score) and negatively affects credit risk (loan loss provision to total liabilities). The findings also show that efficiency has the same effect. The interaction test of bank capital and efficiency shows that efficiency encourages banks to reduce risk, including when bank capital is relatively lower. This finding is expected to have implications for the authorities to boost bank efficiency in addition to establishing several regulations related to capital. The efficiency implemented by the bank will encourage banks to act prudently so that the bank can maintain its performance through risk mitigation.