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

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  • Zhangmei Feng
  • Jiamin Zhang

Abstract

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.

Suggested Citation

  • Zhangmei Feng & Jiamin Zhang, 2022. "Nonparametric K-means algorithm with applications in economic and functional data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(2), pages 537-551, January.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:2:p:537-551
    DOI: 10.1080/03610926.2020.1752383
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    Cited by:

    1. Cheng-Hong Yang & Borcy Lee & Yu-Da Lin, 2022. "Effect of Money Supply, Population, and Rent on Real Estate: A Clustering Analysis in Taiwan," Mathematics, MDPI, vol. 10(7), pages 1-17, April.

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