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Non-parametric kernel regression for multinomial data

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  • Okumura, Hidenori
  • Naito, Kanta

Abstract

This paper presents a kernel smoothing method for multinomial regression. A class of estimators of the regression functions is constructed by minimizing a localized power-divergence measure. These estimators include the bandwidth and a single parameter originating in the power-divergence measure as smoothing parameters. An asymptotic theory for the estimators is developed and the bias-adjusted estimators are obtained. A data-based algorithm for selecting the smoothing parameters is also proposed. Simulation results reveal that the proposed algorithm works efficiently.

Suggested Citation

  • Okumura, Hidenori & Naito, Kanta, 2006. "Non-parametric kernel regression for multinomial data," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 2009-2022, October.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:9:p:2009-2022
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    References listed on IDEAS

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    1. Naito, Kanta, 2001. "On a certain class of nonparametric density estimators with reduced bias," Statistics & Probability Letters, Elsevier, vol. 51(1), pages 71-78, January.
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    Cited by:

    1. Andi Tenri Ampa & I Nyoman Budiantara & Ismaini Zain, 2022. "Modeling the Level of Drinking Water Clarity in Surabaya City Drinking Water Regional Company Using Combined Estimation of Multivariable Fourier Series and Kernel," Sustainability, MDPI, vol. 14(20), pages 1-12, October.

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