Abstract
Standard nonparametric regression estimators such as Nadaraya-Watson, Priestly-Chao, Gasser-Müller, and local linear smoothing have convergence of order O(n -2/5) when the kernel weight functions used are of second order. We discuss here two recently proposed techniques which improve the convergence rate of any given nonparametric regression estimator. When they are applied to the basic O(n -2/5) methods, the convergence rate reduces to O(n -4/9). In this paper, we focus on the cases when these two methods are applied to the local linear smoothing. It is demonstrated by means of a Monte Carlo study that the asymptotic improvements are noticeable even for moderate sample sizes.
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© 1997 Birkhäuser Boston
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Kim, W.C., Park, B.U., Kang, K.H. (1997). On Bias Reduction Methods in Nonparametric Regression Estimation. In: Panchapakesan, S., Balakrishnan, N. (eds) Advances in Statistical Decision Theory and Applications. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-2308-5_12
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DOI: https://doi.org/10.1007/978-1-4612-2308-5_12
Publisher Name: Birkhäuser Boston
Print ISBN: 978-1-4612-7495-7
Online ISBN: 978-1-4612-2308-5
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