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Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data

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Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data

He, Baohua (2009) Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data. Masters thesis, Concordia University.

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Abstract

In this thesis, the CV selection technique is applied into Chaubey, Laib and Sen (2008)'s estimator, which is a new regression estimation for nonnegative random variables. The estimator is based on a generalization of Hille's lemma and a perturbation idea. The first and second order MSE are derived. The ISE criteria for the optimal value of smoothing parameter is discussed and also calculated. The simulation results and the Graphical illustrations on the new estimator, comparing with Fan (1992, 2003)'s local kernel regression estimators are provided.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (Masters)
Authors:He, Baohua
Pagination:v, 44 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M. Sc.
Program:Mathematics
Date:2009
Thesis Supervisor(s):Sen, A
ID Code:976444
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:26
Last Modified:18 Jan 2018 17:42
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