离散面板数据的中位回归

IF 1.1 4区数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Statistical Computation and Simulation Pub Date : 2024-05-15 DOI:10.1080/00949655.2024.2352527

Alfonso Russo, Alessio Farcomeni, Marco Geraci

{"title":"离散面板数据的中位回归","authors":"Alfonso Russo, Alessio Farcomeni, Marco Geraci","doi":"10.1080/00949655.2024.2352527","DOIUrl":null,"url":null,"abstract":"A BSTRACT : We propose a novel method for quantile regression for discrete longitudinal data. The approach is based on the notion of conditional mid-quantiles, which have good theoretical properties even in the presence of ties, and a Ridge-type pe-nalised framework to accommodate dependent data. We illustrate the methods with a simulation study and an original application to the use of macroprudential policies in more than one hundred countries over a period of fifteen years.","PeriodicalId":50040,"journal":{"name":"Journal of Statistical Computation and Simulation","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mid-quantile regression for discrete panel data\",\"authors\":\"Alfonso Russo, Alessio Farcomeni, Marco Geraci\",\"doi\":\"10.1080/00949655.2024.2352527\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A BSTRACT : We propose a novel method for quantile regression for discrete longitudinal data. The approach is based on the notion of conditional mid-quantiles, which have good theoretical properties even in the presence of ties, and a Ridge-type pe-nalised framework to accommodate dependent data. We illustrate the methods with a simulation study and an original application to the use of macroprudential policies in more than one hundred countries over a period of fifteen years.\",\"PeriodicalId\":50040,\"journal\":{\"name\":\"Journal of Statistical Computation and Simulation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Computation and Simulation\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1080/00949655.2024.2352527\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Computation and Simulation","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/00949655.2024.2352527","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

摘要：我们为离散纵向数据的量值回归提出了一种新方法。该方法基于条件中位数的概念，即使在存在联系的情况下也具有良好的理论特性，同时基于一个 Ridge 类型的均衡框架，以适应依赖性数据。我们通过模拟研究和对一百多个国家十五年来宏观审慎政策使用情况的原创应用来说明这些方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Mid-quantile regression for discrete panel data

A BSTRACT : We propose a novel method for quantile regression for discrete longitudinal data. The approach is based on the notion of conditional mid-quantiles, which have good theoretical properties even in the presence of ties, and a Ridge-type pe-nalised framework to accommodate dependent data. We illustrate the methods with a simulation study and an original application to the use of macroprudential policies in more than one hundred countries over a period of fifteen years.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Statistical Computation and Simulation 数学-计算机：跨学科应用

CiteScore

2.30

自引率

8.30%

发文量

156

审稿时长

4-8 weeks

期刊介绍： Journal of Statistical Computation and Simulation ( JSCS ) publishes significant and original work in areas of statistics which are related to or dependent upon the computer. Fields covered include computer algorithms related to probability or statistics, studies in statistical inference by means of simulation techniques, and implementation of interactive statistical systems. JSCS does not consider applications of statistics to other fields, except as illustrations of the use of the original statistics presented. Accepted papers should ideally appeal to a wide audience of statisticians and provoke real applications of theoretical constructions.