Indiana University-Purdue University Indianapolis (IUPUI) / Estimation of nonlinear curves and surfaces has long been the focus of semiparametric
and nonparametric regression. The advances in related model fitting methodology
have greatly enhanced the analyst’s modeling flexibility and have led to scientific discoveries
that would be otherwise missed by the traditional linear model analysis. What has
been less forthcoming are the testing methods concerning nonlinear functions, particularly
for comparisons of curves and surfaces. Few of the existing methods are carefully
disseminated, and most of these methods are subject to important limitations. In the
implementation, few off-the-shelf computational tools have been developed with syntax
similar to the commonly used model fitting packages, and thus are less accessible to
practical data analysts. In this dissertation, I reviewed and tested the existing methods
for nonlinear function comparison, examined their operational characteristics. Some
theoretical justifications were provided for the new testing procedures. Real data exampleswere
included illustrating the use of the newly developed software. A new R package
and a more user-friendly interface were created for enhanced accessibility. / 2020-08-22
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/17235 |
| Date | 31 May 2018 |
| Creators | Zhao, Shi |
| Contributors | Tu, Wanzhu, Bakoyannis, Giorgos, Lourens, Spencer, Song, Yiqing |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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