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Activity Number:
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665
- Regression Methods for Longitudinal Data
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Type:
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Contributed
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Date/Time:
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Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #302952
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Title:
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Variable Bandwidth Kernel Regression Estimation
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Author(s):
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Janet Nakarmi* and Hailin Sang and Lin Ge
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Companies:
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University of Central Arkansas and The University of Mississippi and Mississippi State Univeristy
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Keywords:
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kernel regression estimation;
variable bandwidth;
bias reduction;
central limit theorem
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Abstract:
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In this paper we propose a variable bandwidth kernel regression estimator for i.i.d. observations in R^2 to improve the classical Nadaraya-Watson estimator. The bias is improved to the order of O(h_n^4) under the condition that the fifth order derivative of the density function and the sixth order derivative of the regression function are bounded and uniformly continuous and the dependent variable is bounded. We also establish the central limit theorem for the proposed ideal variable kernel regression estimator. The simulation study confirms our results and demonstrates the advantage of the variable bandwidth kernel method over the classical kernel method.
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Authors who are presenting talks have a * after their name.
