期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2000
卷号：14
期号：2
页码：141-157
出版社：Brazilian Statistical Association
摘要：The t distribution has proved to be a useful alternative to the normal distribution in many econometric regression models, especially when robust estimation is desired. In this work, we consider a nonlinear heteroskedastic Student t regression model. We suppose the observations to be independently t distributed, with the location and scale parameters for each observation being related to linear combinations of some explanatory variables, through regular, and possibly nonlinear, completely known link functions. We obtain the second order biases of the maximum likelihood estimates of the coefficients of those linear combinations and show that the biases will only depend on the first two derivatives of the link functions. We also express the biases in a closed matrix form, allowing them to be easily computed, in practical applications, from auxiliary generalized linear regressions. We discuss some important special cases and present Monte Carlo simulation results indicating that the bias-corrected estimates outperform the corresponding uncorrected estimates for relatively small sample sizes. An example with real data showing the usefulness of bias correction for this model is also presented.
关键词：Bias correction; heteroskedastic model; link function; maximum;likelihood estimate; Student t model.
