Semiparametric maximum likelihood analysis allows inference in errors-invariables
models with small loss of efficiency relative to full likelihood analysis but
with significantly weakened assumptions. In addition, since no distributional
assumptions are made for the nuisance parameters, the analysis more nearly
parallels that for usual regression. These highly desirable features and the high
degree of modelling flexibility permitted warrant the development of the approach
for routine use. This thesis does so for the special cases of linear and nonlinear
regression with measurement errors in one explanatory variable. A transparent and
flexible computational approach is developed, the analysis is exhibited on some
examples, and finite sample properties of estimates, approximate standard errors,
and likelihood ratio inference are clarified with simulation. / Graduation date: 2001
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/32594 |
| Date | 03 May 2001 |
| Creators | Suh, Eun-Young |
| Contributors | Schafer, Daniel W. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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