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Memorial University - Electronic Theses and Dissertations 4
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Document Description
TitleA comparison of nonlinear and nonparametric regression methods
AuthorChen, Min, 1981-
DescriptionThesis (M.A.S.)--Memorial University of Newfoundland, 2010. Mathematics and Statistics
Paginationvii, 49 leaves : ill.
SubjectRegression analysis--Mathematical models--Evaluation; Smoothing (Statistics);
Degree GrantorMemorial University of Newfoundland. Dept. of Mathematics and Statistics
DisciplineMathematics and Statistics
NotesIncludes bibliographical references (leaves 48-49)
AbstractIn this report, we investigate the performance of nonlinear regression and nonparametric regression with data set simulated under a nonlinear parametric model. First, we consider the nonlinear least squares estimation method for the model. Then, we apply various nonparametric regression methods such as kernel methods, spline smoothing, and wavelet version of estimators with the same model. The nonlinear least squares estimation method produces the best estimation in terms of MSE among all the regression methods. Both kernel methods and wavelet version of estimation methods produce reasonably small values of MSE. Moreover, the wavelet regression method performances best among all the nonparametric methods. The spline method produces unacceptably large MSE due to large variance of estimation. The boundary issues do exist in all the nonparametric regression methods due to less density of data points.
Resource TypeElectronic thesis or dissertation
FormatImage/jpeg; Application/pdf
SourcePaper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifiera3295732
RightsThe 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.
CollectionElectronic Theses and Dissertations
Scanning StatusCompleted
PDF File(7.94 MB) --
CONTENTdm file name41524.cpd