Algorithms for clustering data
Algorithms for clustering data
Empirical likelihood confidence intervals for linear regression coefficients
Journal of Multivariate Analysis
Robust mixture modeling based on scale mixtures of skew-normal distributions
Computational Statistics & Data Analysis
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There is a vast variety of clustering methods available in the literature. The performance of many of them strongly depends on specific patterns in data. This paper introduces a clustering procedure based on the empirical likelihood method which inherits many advantages of the classical likelihood approach without imposing restrictive probability distribution constraints. The performance of the proposed procedure is illustrated on simulated and classification datasets with excellent results. The comparison of the algorithm with several well-known clustering methods is very encouraging. The procedure is more robust and has higher accuracy than the competitors.