具有不等式约束的多元线性模型下的安全带估计量

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference Pub Date : 2025-08-20 DOI:10.1016/j.jspi.2025.106335

Katarzyna Filipiak , Dietrich von Rosen , Wojciech Rejchel , Martin Singull

{"title":"具有不等式约束的多元线性模型下的安全带估计量","authors":"Katarzyna Filipiak , Dietrich von Rosen , Wojciech Rejchel , Martin Singull","doi":"10.1016/j.jspi.2025.106335","DOIUrl":null,"url":null,"abstract":"<div><div>The main goal of this paper is to determine maximum likelihood estimators under a multivariate linear model with prior information introduced via inequality restrictions on the mean parameters. The restrictions are in the form of quadratic inequalities. Methods from convex optimization theory play a fundamental role in determining the estimators. A characteristic of the new estimators, called Safety Belt estimators, is that depending on the observed data, there are two alternative solutions to the likelihood equations.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"241 ","pages":"Article 106335"},"PeriodicalIF":0.8000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Safety Belt estimator under multivariate linear models with inequality constraints\",\"authors\":\"Katarzyna Filipiak , Dietrich von Rosen , Wojciech Rejchel , Martin Singull\",\"doi\":\"10.1016/j.jspi.2025.106335\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The main goal of this paper is to determine maximum likelihood estimators under a multivariate linear model with prior information introduced via inequality restrictions on the mean parameters. The restrictions are in the form of quadratic inequalities. Methods from convex optimization theory play a fundamental role in determining the estimators. A characteristic of the new estimators, called Safety Belt estimators, is that depending on the observed data, there are two alternative solutions to the likelihood equations.</div></div>\",\"PeriodicalId\":50039,\"journal\":{\"name\":\"Journal of Statistical Planning and Inference\",\"volume\":\"241 \",\"pages\":\"Article 106335\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2025-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Planning and Inference\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378375825000734\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Planning and Inference","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378375825000734","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}

引用次数: 0

摘要

本文的主要目的是确定多元线性模型下的最大似然估计量，该模型通过对平均参数的不等式限制引入先验信息。这些限制是以二次不等式的形式出现的。凸优化理论的方法在确定估计量方面起着重要的作用。这种被称为安全带估计器的新估计器的一个特点是，根据观测到的数据，似然方程有两个可选的解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

The Safety Belt estimator under multivariate linear models with inequality constraints

The main goal of this paper is to determine maximum likelihood estimators under a multivariate linear model with prior information introduced via inequality restrictions on the mean parameters. The restrictions are in the form of quadratic inequalities. Methods from convex optimization theory play a fundamental role in determining the estimators. A characteristic of the new estimators, called Safety Belt estimators, is that depending on the observed data, there are two alternative solutions to the likelihood equations.

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 Planning and Inference 数学-统计学与概率论

CiteScore

2.10

自引率

11.10%

发文量

审稿时长

3-6 weeks

期刊介绍： The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists. We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.