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Non-parametric kernel regression for multinomial data

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  • Okumura, Hidenori
  • Naito, Kanta
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    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.

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    File URL: http://www.sciencedirect.com/science/article/B6WK9-4KBVV4G-6/2/5714fa3f28205296afa0128cd7de855d
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 97 (2006)
    Issue (Month): 9 (October)
    Pages: 2009-2022

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    Handle: RePEc:eee:jmvana:v:97:y:2006:i:9:p:2009-2022

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    Related research

    Keywords: Non-parametric regression Multinomial data Kernel smoothing Power-divergence measure;

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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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