Home List of Titles Parameter estimation in semi-linear models using a maximal invariant likelihood function
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/45320
- Parameter estimation in semi-linear models using a maximal invariant likelihood function
- Bhowmik, Jahar L.; King, Maxwell L.
- In this paper, we consider the problem of estimation of semi-linear regression models. Using invariance arguments, Bhowmik and King [2007. Maximal invariant likelihood based testing of semi-linear models. Statist. Papers 48, 357-383] derived the probability density function of the maximal invariant statistic for the non-linear component of these models. Using this density function as a likelihood function allows us to estimate these models in a two-step process. First the non-linear component parameters are estimated by maximising the maximal invariant likelihood function. Then the non-linear component, with the parameter values replaced by estimates, is treated as a regressor and ordinary least squares is used to estimate the remaining parameters. We report the results of a simulation study conducted to compare the accuracy of this approach with full maximum likelihood and maximum profile-marginal likelihood estimation. We find maximising the maximal invariant likelihood function typically results in less biased and lower variance estimates than those from full maximum likelihood.
- Publication type
- Journal article
- Research centre
- Swinburne University of Technology. Faculty of Life and Social Sciences
- Journal of Statistical Planning and Inference, Vol. 139, no. 4 (Apr 2009), pp. 1276-1285
- Publication year
- Maximum likelihood estimation; Non-linear modelling; Simulation experiment; Two-step estimation
- Elsevier B.V.
- Publisher URL
- Copyright © 2008 Elsevier B.V. All rights reserved.
- Peer reviewed