Wavelet designs for nonparametric regression models with autocorrelated errors

Selvaratnam, Selvakkadunko (2011) Wavelet designs for nonparametric regression models with autocorrelated errors. Masters thesis, Memorial University of Newfoundland.

[img] [English] PDF - Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Download (17Mb)

Abstract

We consider minimax designs for estimation of nonparametric regression models using wavelet approximations of the mean response function. We assume that the error terms are autocorrelated. Since the method of estimation depends on the choice of design, we argue that using ordinary least squares method (OLS) for estimation may lead to designs that are less efficient than designs constructed based on generalized least squares (GLS) or weighted least squares (WLS). A simulated annealing algorithm is developed to carry out the minimization problems to search for minimax designs. In this thesis we considered AR(1) model for example. We found that the GLS method is good for the moderate level correlation and WLS or OLS is preferred for highly correlated data.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/11396
Item ID: 11396
Additional Information: Includes bibliographical references (leaves 116-119).
Department(s): Science, Faculty of > Mathematics and Statistics
Date: 2011
Date Type: Submission
Library of Congress Subject Heading: Wavelets (Mathematics); Regression analysis--Mathematical models.

Actions (login required)

View Item View Item

Downloads

Downloads per month over the past year

View more statistics