This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies.
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/3126 |
| Date | 21 January 2009 |
| Creators | Abarin, Taraneh |
| Contributors | Wang, Liqun (Statistics), John Brewster (Statistics), James Fu (Statistics), Gady Jacoby (Asper School of Business), Julie Zhou (Mathematics and Statistics, University of Victoria) |
| Source Sets | University of Manitoba Canada |
| Language | en_US |
| Detected Language | English |
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