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Document Description
Title
A
comparison
of
nonlinear
and
nonparametric
regression
methods
Author
Chen
,
Min
,
1981-
Description
Thesis
(M.A.S.)--Memorial
University
of
Newfoundland
,
2010.
Mathematics
and
Statistics
Date
2010
Pagination
vii, 49 leaves : ill.
Subject
Regression
analysis--Mathematical
models--Evaluation;
Smoothing
(Statistics);
Degree
M.A.S.
Degree Grantor
Memorial University of Newfoundland. Dept. of Mathematics and Statistics
Discipline
Mathematics and Statistics
Language
Eng
Notes
Includes
bibliographical
references
(leaves
48-49)
Abstract
In 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.
Type
Text
Resource Type
Electronic
thesis
or
dissertation
Format
Image/jpeg;
Application/pdf
Source
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifier
a3295732
Rights
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.
Collection
Electronic
Theses
and
Dissertations
Scanning Status
Completed
PDF File
(7.94
MB)
--
http://collections.mun.ca/PDFs/theses/Chen_Min.pdf
CONTENTdm file name
41524.cpd
