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

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

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 ar...

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Main Author: He, Baohua
Format: Others
Published: 2009
Online Access:http://spectrum.library.concordia.ca/976444/1/MR63095.pdf
He, Baohua <http://spectrum.library.concordia.ca/view/creators/He=3ABaohua=3A=3A.html> (2009) Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data. Masters thesis, Concordia University.
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spelling ndltd-LACETR-oai-collectionscanada.gc.ca-QMG.9764442013-10-22T03:48:14Z Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data He, Baohua 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. 2009 Thesis NonPeerReviewed application/pdf http://spectrum.library.concordia.ca/976444/1/MR63095.pdf He, Baohua <http://spectrum.library.concordia.ca/view/creators/He=3ABaohua=3A=3A.html> (2009) Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data. Masters thesis, Concordia University. http://spectrum.library.concordia.ca/976444/
collection NDLTD
format Others
sources NDLTD
description 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.
author He, Baohua
spellingShingle He, Baohua
Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data
author_facet He, Baohua
author_sort He, Baohua
title Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data
title_short Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data
title_full Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data
title_fullStr Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data
title_full_unstemmed Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data
title_sort smoothing parameter selection for a new regression estimator for non-negative data
publishDate 2009
url http://spectrum.library.concordia.ca/976444/1/MR63095.pdf
He, Baohua <http://spectrum.library.concordia.ca/view/creators/He=3ABaohua=3A=3A.html> (2009) Smoothing Parameter Selection For A New Regression Estimator For Non-Negative Data. Masters thesis, Concordia University.
work_keys_str_mv AT hebaohua smoothingparameterselectionforanewregressionestimatorfornonnegativedata
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