论文标题
Beta Prime回归模型中改进的估计器
Improved estimators in beta prime regression models
论文作者
论文摘要
在本文中，我们考虑了\ cite {bour18}最近提出的beta素回归模型，该模型量身定制，该模型是针对响应连续且限于偏斜和长尾巴的正面真实线的情况，而回归结构涉及回归和未知参数。我们考虑了对模型索引的参数的最大样品估计器的两种不同的偏差校正策略。特别是，我们讨论了模型的平均值和分散参数的偏差校正估计器。此外，作为讨论的两个分析偏差校正估计器的替代方法，我们考虑了基于参数引导程序的偏置校正机制。数值结果表明，偏置校正方案产生几乎无偏的估计值。提出和讨论了一个具有真实数据的示例。
In this paper, we consider the beta prime regression model recently proposed by \cite{bour18}, which is tailored to situations where the response is continuous and restricted to the positive real line with skewed and long tails and the regression structure involves regressors and unknown parameters. We consider two different strategies of bias correction of the maximum-likelihood estimators for the parameters that index the model. In particular, we discuss bias-corrected estimators for the mean and the dispersion parameters of the model. Furthermore, as an alternative to the two analytically bias-corrected estimators discussed, we consider a bias correction mechanism based on the parametric bootstrap. The numerical results show that the bias correction scheme yields nearly unbiased estimates. An example with real data is presented and discussed.
