This thesis advocates the use of maximum likelihood analysis for generalized
regression models with measurement error in a single explanatory variable. This will be
done first by presenting a computational algorithm and the numerical details for carrying
out this algorithm on a wide variety of models. The computational methods will be based
on the EM algorithm in conjunction with the use of Gauss-Hermite quadrature to
approximate integrals in the E-step. Second, this thesis will demonstrate the relative
superiority of likelihood-ratio tests and confidence intervals over those based on
asymptotic normality of estimates and standard errors, and that likelihood methods may
be more robust in these situations than previously thought. The ability to carry out
likelihood analysis under a wide range of distributional assumptions, along with the
advantages of likelihood ratio inference and the encouraging robustness results make
likelihood analysis a practical option worth considering in regression problems with
explanatory variable measurement error. / Graduation date: 1999
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/33876 |
| Date | 23 September 1998 |
| Creators | Higdon, Roger |
| Contributors | Schafer, Daniel W. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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