Non-parametric kernel regression for multinomial data

Hidenori Okumura and Kanta Naito

Journal of Multivariate Analysis, 2006, vol. 97, issue 9, 2009-2022

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.

Keywords: Non-parametric; regression; Multinomial; data; Kernel; smoothing; Power-divergence; measure (search for similar items in EconPapers)
Date: 2006
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