A generic algorithm for reducing bias in parametric estimation
Open Access
- 1 January 2010
- journal article
- Published by Institute of Mathematical Statistics in Electronic Journal of Statistics
- Vol. 4 (none), 1097-1112
- https://doi.org/10.1214/10-ejs579
Abstract
A general iterative algorithm is developed for the computation of reduced-bias parameter estimates in regular statistical models through adjustments to the score function. The algorithm unifies and provides appealing new interpretation for iterative methods that have been published previously for some specific model classes. The new algorithm can usefully be viewed as a series of iterative bias corrections, thus facilitating the adjusted score approach to bias reduction in any model for which the first-order bias of the maximum likelihood estimator has already been derived. The method is tested by application to a logit-linear multiple regression model with beta-distributed responses; the results confirm the effectiveness of the new algorithm, and also reveal some important errors in the existing literature on beta regression.Keywords
This publication has 27 references indexed in Scilit:
- Bias reduction in exponential family nonlinear modelsBiometrika, 2009
- Bias Correction in Generalized Nonlinear Models with Dispersion CovariatesCommunications in Statistics - Theory and Methods, 2008
- A Solution to Separation in Binary Response ModelsPolitical Analysis, 2005
- A solution to the problem of separation in logistic regressionStatistics in Medicine, 2002
- Bias correction for a class of multivariate nonlinear regression modelsStatistics & Probability Letters, 1997
- Bias Correction in Generalized Linear Mixed Models with Multiple Components of DispersionJournal of the American Statistical Association, 1996
- Likelihood-Based Methods for Bias Reduction in Limiting Dilution AssaysBiometrics, 1995
- Bias reduction of maximum likelihood estimatesBiometrika, 1993
- Bias in nonlinear regressionBiometrika, 1986
- On the existence of maximum likelihood estimates in logistic regression modelsBiometrika, 1984