Modified Likelihood and Related Methods for Handling Nuisance Parameters in the Linear Regression Model
AbstractIn this paper, different approaches to dealing with nuisance parameters in the likelihood based inference are presented and illustrated by reference to the linear regression model with nonspherical errors. The estimator of the error variance using each of the approaches is also derived for the linear regression model with spherical erors. We observe that many of these estimators are unbiased. A theoretical comparison of the likelihood functions is reported and we note that some of them are equivalent.
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Bibliographic InfoPaper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 5/98.
Length: 37 pages
Date of creation: 1998
Date of revision:
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Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Web page: http://www.buseco.monash.edu.au/depts/ebs/
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- C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
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- Jahar Bhowmik & Maxwell King, 2007.
"Maximal invariant likelihood based testing of semi-linear models,"
Springer, vol. 48(3), pages 357-383, September.
- Maxwell L. King & Jahar L. Bhowmik, 2004. "Maximal Invariant Likelihood Based Testing of Semi-Linear Models," Econometric Society 2004 Australasian Meetings 245, Econometric Society.
- Jahar L. Bhowmik & Maxwell L. King, 2005. "Parameter Estimation in Semi-Linear Models Using a Maximal Invariant Likelihood Function," Monash Econometrics and Business Statistics Working Papers 18/05, Monash University, Department of Econometrics and Business Statistics.
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