On outliers detection and prior distribution sensitivity in standard skew-probit regression models

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Fabiano Rodrigues Coelho, C. Russo, J. Bazán
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引用次数: 2

Abstract

Regression models with probit and logit link functions are the most frequently used for binary response variables. However, traditional approaches may not be adequate when data are unbalanced. This paper deals with standard skew-probit regression models. Parameters were estimated through a new Bayesian approach which consists of the use of Hamiltonian Monte Carlo (HMC) and the original likelihood function. Simulation studies assessed the efficiency of the estimation method and the sensitivity of prior distributions for parameters related to asymmetry calculating the RMSE (root mean square error). The proposed estimation method was compared when used for detecting outliers. The results show that the proposed method is more efficient than INLA and is successful in the recovery of true parameter values. The sensitivity study enabled the proposal of a new prior distribution configuration for the asymmetry parameter, and the randomized quantile residual proved to be more suitable for detecting outliers. The methodology was applied to a diabetes dataset towards illustrating the results.
标准斜概率回归模型的异常值检测和先验分布灵敏度
对于二元响应变量,最常用的是带有probit和logit链接函数的回归模型。然而,当数据不平衡时,传统的方法可能不足够。本文研究标准的斜概率回归模型。通过一种新的贝叶斯方法估计参数,该方法由哈密顿蒙特卡罗(HMC)和原始似然函数的使用组成。仿真研究评估了估计方法的效率和先验分布对计算RMSE(均方根误差)相关参数的敏感性。将所提出的估计方法用于检测异常值时进行了比较。结果表明,该方法比INLA方法更有效,能够成功地恢复真实参数值。灵敏度研究为不对称参数提出了新的先验分布配置,随机分位数残差更适合检测异常值。该方法被应用于糖尿病数据集,以说明结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.60
自引率
10.00%
发文量
30
审稿时长
>12 weeks
期刊介绍: The Brazilian Journal of Probability and Statistics aims to publish high quality research papers in applied probability, applied statistics, computational statistics, mathematical statistics, probability theory and stochastic processes. More specifically, the following types of contributions will be considered: (i) Original articles dealing with methodological developments, comparison of competing techniques or their computational aspects. (ii) Original articles developing theoretical results. (iii) Articles that contain novel applications of existing methodologies to practical problems. For these papers the focus is in the importance and originality of the applied problem, as well as, applications of the best available methodologies to solve it. (iv) Survey articles containing a thorough coverage of topics of broad interest to probability and statistics. The journal will occasionally publish book reviews, invited papers and essays on the teaching of statistics.
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