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Inspired by the well-known relationship between K-means algorithm and Expectation-Maximization (EM) algorithm for mixture models, we propose nonparametric K-means algorithm for estimation of nonparametric mixture of regressions and mixture of Gaussian processes. The proposed methods are illustrated by extensive numerical simulations, comparisons, and analysis of two real datasets. Simulation studies and applications demonstrate that our method is an effective and competitive procedure for modified EM algorithm in nonparametric mixture settings.

Nonparametric K-means algorithm with applications in economic and functional data