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Highly Efficient Estimators with High Breakdown Point for Linear Models with Structured Covariance Matrices 结构协方差矩阵线性模型的高效高断点估计
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2023-04-02 DOI: 10.1016/j.ecosta.2023.03.003
Hendrik Paul Lopuhaä
{"title":"Highly Efficient Estimators with High Breakdown Point for Linear Models with Structured Covariance Matrices","authors":"Hendrik Paul Lopuhaä","doi":"10.1016/j.ecosta.2023.03.003","DOIUrl":"10.1016/j.ecosta.2023.03.003","url":null,"abstract":"<div><div>A unified approach is provided for a method of estimation of the regression parameter in balanced linear models with a structured covariance matrix that combines a high breakdown point with high asymptotic efficiency at models with multivariate normal errors. Of main interest are linear mixed effects models, but our approach also includes several other standard multivariate models, such as multiple regression, multivariate regression, and multivariate location and scatter. Sufficient conditions are provided for the existence of the estimators and corresponding functionals, strong consistency and asymptotic normality is established, and robustness properties are derived in terms of breakdown point and influence function. All the results are obtained for general identifiable covariance structures and are established under mild conditions on the distribution of the observations, which goes far beyond models with elliptically contoured densities. Some results are new and others are more general than existing ones in the literature. In this way, results on high breakdown estimation with high efficiency in a wide variety of multivariate models are completed and improved.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 53-73"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73044459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Challenges of cellwise outliers 单元离群值的挑战
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2024-02-17 DOI: 10.1016/j.ecosta.2024.02.002
Jakob Raymaekers , Peter J. Rousseeuw
{"title":"Challenges of cellwise outliers","authors":"Jakob Raymaekers ,&nbsp;Peter J. Rousseeuw","doi":"10.1016/j.ecosta.2024.02.002","DOIUrl":"10.1016/j.ecosta.2024.02.002","url":null,"abstract":"<div><div>It is well-known that real data often contain outliers. The term outlier usually refers to a case, usually denoted by a row of the <span><math><mrow><mi>n</mi><mo>×</mo><mi>d</mi></mrow></math></span> data matrix. In recent times a different type has come into focus, the cellwise outliers. These are suspicious cells (entries) that can occur anywhere in the data matrix. Even a relatively small proportion of outlying cells can contaminate over half the cases, which is a problem for robust methods. This article discusses the challenges posed by cellwise outliers, and some methods developed so far to deal with them. New results are obtained on cellwise breakdown values for location, covariance and regression. A cellwise robust method is proposed for correspondence analysis, with real data illustrations. The paper concludes by formulating some points for debate.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 6-25"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139920276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Normality testing after outlier removal 去除异常值后的正态性检验
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2023-06-08 DOI: 10.1016/j.ecosta.2023.06.001
Vanessa Berenguer-Rico , Bent Nielsen
{"title":"Normality testing after outlier removal","authors":"Vanessa Berenguer-Rico ,&nbsp;Bent Nielsen","doi":"10.1016/j.ecosta.2023.06.001","DOIUrl":"10.1016/j.ecosta.2023.06.001","url":null,"abstract":"<div><div>The cumulant based normality test after outlier removal is analyzed. It is shown that the standard least squares normalizations can be misleading in this context. The sample cumulants should be standardized according to the truncation imposed at the removal stage and the estimation method being used. New standardizations that lead to chi-squared inference are derived.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 74-96"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75616568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust logistic regression for ordered and unordered responses 有序和无序响应的鲁棒逻辑回归
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2023-06-02 DOI: 10.1016/j.ecosta.2023.05.004
Maria Iannario , Anna Clara Monti
{"title":"Robust logistic regression for ordered and unordered responses","authors":"Maria Iannario ,&nbsp;Anna Clara Monti","doi":"10.1016/j.ecosta.2023.05.004","DOIUrl":"10.1016/j.ecosta.2023.05.004","url":null,"abstract":"<div><div>Multinomial regression models and cumulative, adjacent-categories and continuation-ratio models are applied in many fields to analyze unordered or ordered responses with respect to subjects’ profiles. They are typically fitted by maximum likelihood estimators, which unfortunately are sensitive to anomalous data. In order to cope with these data robust <span><math><mi>M</mi></math></span> type estimators can be applied. They exploit the properties of the logistic link function and are based on a weighted likelihood approach. The <span><math><mi>M</mi></math></span> estimators can be easily implemented numerically, provide reliable inference when data are contaminated and lead to an accurate model specification. Inference based on the <span><math><mi>M</mi></math></span> estimators is illustrated in three case studies related to risk attitude in financial investments, diabetes in non-obese adult patients and intensity of chronic pain in aging people.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 97-121"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89371091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Robust Quantitative Risk Screening for Subgroup Pursuit in Clinical Trials 临床试验中亚组追求的可靠定量风险筛选
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2023-06-20 DOI: 10.1016/j.ecosta.2023.05.002
Xinzhou Guo , Ruosha Li , Jianjun Zhou , Xuming He
{"title":"A Robust Quantitative Risk Screening for Subgroup Pursuit in Clinical Trials","authors":"Xinzhou Guo ,&nbsp;Ruosha Li ,&nbsp;Jianjun Zhou ,&nbsp;Xuming He","doi":"10.1016/j.ecosta.2023.05.002","DOIUrl":"10.1016/j.ecosta.2023.05.002","url":null,"abstract":"<div><div>In clinical studies, when to recommend or decide further pursuit of the most promising subgroup that has been observed from an existing trial is a very important question. It is well recognized that the working models in assessing subgroup effects might be misspecified and the observed treatment effect size of the best selected subgroup tends to be too optimistic. Therefore, a careful and robust statistical quantification of risk is useful before any decision of subgroup pursuit is made. Via the newly established bootstrap consistency for the misspecified proportional hazard model, the issue of selection bias and model misspecification in subgroup pursuit is addressed, and a robust risk quantitative measure directly based on the observed treatment effect of the selected subgroup that might be used in the decision-making of subgroup pursuit is provided. Two earlier studies are reviewed to demonstrate what can be learned from the proposed risk index.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 122-141"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82842323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Using Canonical Quantile Regression to predict company performance: better prediction than using CEO compensation 用标准分位数回归预测公司绩效：比用CEO薪酬预测更好
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2022-10-26 DOI: 10.1016/j.ecosta.2022.10.002
Stephen Portnoy , Yossi Haimberg
{"title":"Using Canonical Quantile Regression to predict company performance: better prediction than using CEO compensation","authors":"Stephen Portnoy ,&nbsp;Yossi Haimberg","doi":"10.1016/j.ecosta.2022.10.002","DOIUrl":"10.1016/j.ecosta.2022.10.002","url":null,"abstract":"<div><div>In using multiple regression<span><span> methods for prediction, one often considers the linear combination<span><span> of explanatory variables as an index. Seeking a single such index when there are multiple responses is rather more complicated. One classical approach is to use the coefficients from the leading </span>Canonical Correlation. However, methods based on variances are unable to disaggregate responses by </span></span>quantile<span> effects, lack robustness, and rely on normal assumptions for inference. To address these problems, a novel regression quantile approach will be applied to an empirical study of the performance of large publicly held companies and CEO compensation.</span></span></div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 42-52"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86613808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust empirical risk minimization via Newton’s method 基于牛顿方法的鲁棒经验风险最小化
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2023-07-20 DOI: 10.1016/j.ecosta.2023.07.003
Eirini Ioannou , Muni Sreenivas Pydi , Po-Ling Loh
{"title":"Robust empirical risk minimization via Newton’s method","authors":"Eirini Ioannou ,&nbsp;Muni Sreenivas Pydi ,&nbsp;Po-Ling Loh","doi":"10.1016/j.ecosta.2023.07.003","DOIUrl":"10.1016/j.ecosta.2023.07.003","url":null,"abstract":"<div><div>A new variant of Newton’s method for empirical risk minimization is studied, where at each iteration of the optimization algorithm, the gradient and Hessian of the objective function are replaced by robust estimators taken from existing literature on robust mean estimation for multivariate data. After proving a general theorem about the convergence of successive iterates to a small ball around the population-level minimizer, consequences of the theory in generalized linear models are studied when data are generated from Huber’s epsilon-contamination model and/or heavy-tailed distributions. An algorithm for obtaining robust Newton directions based on the conjugate gradient method is also proposed, which may be more appropriate for high-dimensional settings, and conjectures about the convergence of the resulting algorithm are offered. Compared to robust gradient descent, the proposed algorithm enjoys the faster rates of convergence for successive iterates often achieved by second-order algorithms for convex problems, i.e., quadratic convergence in a neighborhood of the optimum, with a stepsize that may be chosen adaptively via backtracking linesearch.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 142-168"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135568274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Rejoinder to the comment of Agostinelli 对阿戈斯蒂内利评论的反驳
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2024-02-17 DOI: 10.1016/j.ecosta.2024.02.004
Jakob Raymaekers , Peter J. Rousseeuw
{"title":"Rejoinder to the comment of Agostinelli","authors":"Jakob Raymaekers ,&nbsp;Peter J. Rousseeuw","doi":"10.1016/j.ecosta.2024.02.004","DOIUrl":"10.1016/j.ecosta.2024.02.004","url":null,"abstract":"","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 29-30"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139920448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analyzing cellwise weighted data 分析单元格加权数据
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2023-02-06 DOI: 10.1016/j.ecosta.2023.01.007
Peter J. Rousseeuw
{"title":"Analyzing cellwise weighted data","authors":"Peter J. Rousseeuw","doi":"10.1016/j.ecosta.2023.01.007","DOIUrl":"10.1016/j.ecosta.2023.01.007","url":null,"abstract":"<div><div>Often the rows (cases, objects) of a dataset have weights. For instance, the weight of a case may reflect the number of times it has been observed, or its reliability. For analyzing such data many rowwise weighted techniques are available, the most well known being the weighted average. But there are also situations where the individual <em>cells</em> (entries) of the data matrix have weights assigned to them. An approach to analyze such data is proposed. A cellwise weighted likelihood function is defined, that corresponds to a transformation of the dataset which is called unpacking. Using this weighted likelihood one can carry out multivariate statistical methods such as maximum likelihood estimation and likelihood ratio tests. Particular attention is paid to the estimation of covariance matrices, because these are the building blocks of much of multivariate statistics. An <span>R</span> implementation of the cellwise maximum likelihood estimator is provided, which employs a version of the EM algorithm. Also a faster approximate method is proposed, which is asymptotically equivalent to it.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 31-41"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73373319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comments on “Challenges of cellwise outliers” by Jakob Raymaekers and Peter J. Rousseeuw 对 Jakob Raymaekers 和 Peter J. Rousseeuw 所作 "细胞离群值的挑战 "的评论
IF 2.5
Econometrics and Statistics Pub Date : 2026-04-01 Epub Date: 2024-02-24 DOI: 10.1016/j.ecosta.2024.02.003
Claudio Agostinelli
{"title":"Comments on “Challenges of cellwise outliers” by Jakob Raymaekers and Peter J. Rousseeuw","authors":"Claudio Agostinelli","doi":"10.1016/j.ecosta.2024.02.003","DOIUrl":"10.1016/j.ecosta.2024.02.003","url":null,"abstract":"<div><div>The main aim of robust statistics is the development of methods able to cope with the presence of outliers. A new type of outliers, namely “cellwise”, has garnered considerable attention. The state of the art for dealing with cellwise contamination in different models is presented in Raymaekers and Rousseeuw (in press). Outliers in time series can be treated as cellwise outliers, a further discussion on this subject is presented.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"38 ","pages":"Pages 26-28"},"PeriodicalIF":2.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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