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Can I read The Iteratively Reweighted Estimating Equation in Minimum Distance Problems on EtoBox?
The Iteratively Reweighted Estimating Equation in Minimum Distance Problems by Ayanendranath Basu; Bruce G. Lindsay is a Mathematics article available to read on EtoBox.
What is The Iteratively Reweighted Estimating Equation in Minimum Distance Problems about?
The class of density based minimum distance estimators provide attractive alternatives to the maximum likelihood estimator because several members of this class have nice robustness properties while being ÿrst-order e cient under the assumed model. A helpful computational technique-similar to the iteratively reweighted least squares used in robust regression-is introduced which makes these estimators computationally much more feasible. This technique is much simpler than the Newton-Raphson (NR) method to implement. The loss su ered in the rate of convergence compared to the NR method can be made to vanish in some exponential family situations by a little modiÿcation in the weight function-in which case the performance is comparable to the NR method. For a large number of parameters the performance of this modiÿed version is actually expected to be better than the NR method. In view of the widespread interest in density based robust procedures, this modiÿcation appears to be of great practical value.
Who reads The Iteratively Reweighted Estimating Equation in Minimum Distance Problems?
It is typically read by researchers, students, and practitioners in Mathematics.
- Author
- Ayanendranath Basu; Bruce G. Lindsay
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0167-9473)
- Published
- 2004
- Language
- EN
- Field
- Mathematics (Physical Sciences)