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

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY Brazilian Journal of Probability and Statistics Pub Date : 2022-09-01 DOI:10.1214/22-bjps534
Fabiano Rodrigues Coelho, C. Russo, J. Bazán
{"title":"On outliers detection and prior distribution sensitivity in standard skew-probit regression models","authors":"Fabiano Rodrigues Coelho, C. Russo, J. Bazán","doi":"10.1214/22-bjps534","DOIUrl":null,"url":null,"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.","PeriodicalId":51242,"journal":{"name":"Brazilian Journal of Probability and Statistics","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brazilian Journal of Probability and Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1214/22-bjps534","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
Multivariate zero-inflated Bell–Touchard distribution for multivariate counts: An application to COVID-related data Unit gamma regression models for correlated bounded data Two-stage Walsh-average-based robust estimation and variable selection for partially linear additive spatial autoregressive models On quasi Pólya thinning operator Divide-and-conquer Metropolis–Hastings samplers with matched samples
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1