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The Kendall and Spearman rank correlations of the bivariate skew normal distribution 二元偏斜正态分布的Kendall和Spearman秩相关性
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-12-01 DOI: 10.1111/sjos.12587
Andréas Heinen, Alfonso Valdesogo
{"title":"The Kendall and Spearman rank correlations of the bivariate skew normal distribution","authors":"Andréas Heinen, Alfonso Valdesogo","doi":"10.1111/sjos.12587","DOIUrl":"https://doi.org/10.1111/sjos.12587","url":null,"abstract":"We derive the Kendall and Spearman rank correlation coefficients of the bivariate skew normal (SN) distribution. For a given correlation parameter, we provide conditions on the shape parameters, under which the SN is more dependent than the normal in terms of each of the two‐rank correlations. We further show how our results can be used for rank‐based estimation procedures of the correlation parameter and the equal shape parameter of the SN, whose consistency and asymptotic normality we establish.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"49 1","pages":"1669 - 1698"},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42608100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Issue Information 问题信息
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-11-16 DOI: 10.1111/sjos.12536
{"title":"Issue Information","authors":"","doi":"10.1111/sjos.12536","DOIUrl":"https://doi.org/10.1111/sjos.12536","url":null,"abstract":"","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46923490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Distributed inference for two‐sample U‐statistics in massive data analysis 海量数据分析中两样本U统计量的分布式推断
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-10-06 DOI: 10.1111/sjos.12620
Bingyao Huang, Yanyan Liu, Liuhua Peng
{"title":"Distributed inference for two‐sample U‐statistics in massive data analysis","authors":"Bingyao Huang, Yanyan Liu, Liuhua Peng","doi":"10.1111/sjos.12620","DOIUrl":"https://doi.org/10.1111/sjos.12620","url":null,"abstract":"This paper considers distributed inference for two‐sample U‐statistics under the massive data setting. In order to reduce the computational complexity, this paper proposes distributed two‐sample U‐statistics and blockwise linear two‐sample U‐statistics. The blockwise linear two‐sample U‐statistic, which requires less communication cost, is more computationally efficient especially when the data are stored in different locations. The asymptotic properties of both types of distributed two‐sample U‐statistics are established. In addition, this paper proposes bootstrap algorithms to approximate the distributions of distributed two‐sample U‐statistics and blockwise linear two‐sample U‐statistics for both nondegenerate and degenerate cases. The distributed weighted bootstrap for the distributed two‐sample U‐statistic is new in the literature. The proposed bootstrap procedures are computationally efficient and are suitable for distributed computing platforms with theoretical guarantees. Extensive numerical studies illustrate that the proposed distributed approaches are feasible and effective.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"1090 - 1115"},"PeriodicalIF":1.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43213317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes 低维和高维非齐次吉布斯点过程的推论
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-09-30 DOI: 10.1111/sjos.12616
Ismaila Ba, Jean‐François Coeurjolly
{"title":"Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes","authors":"Ismaila Ba, Jean‐François Coeurjolly","doi":"10.1111/sjos.12616","DOIUrl":"https://doi.org/10.1111/sjos.12616","url":null,"abstract":"Gibbs point processes (GPPs) constitute a large and flexible class of spatial point processes with explicit dependence between the points. They can model attractive as well as repulsive point patterns. Feature selection procedures are an important topic in high‐dimensional statistical modeling. In this paper, a composite likelihood (in particular pseudo‐likelihood) approach regularized with convex and nonconvex penalty functions is proposed to handle statistical inference for possibly high‐dimensional inhomogeneous GPPs. We particularly investigate the setting where the number of covariates diverges as the domain of observation increases. Under some conditions provided on the spatial GPP and on penalty functions, we show that the oracle property, consistency and asymptotic normality hold. Our results also cover the low‐dimensional case which fills a large gap in the literature. Through simulation experiments, we validate our theoretical results and finally, an application to a tropical forestry dataset illustrates the use of the proposed approach.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"1021 - 993"},"PeriodicalIF":1.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45966747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
On the robustness to outliers of the Student‐t process 关于Student - t过程对异常值的鲁棒性
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-09-06 DOI: 10.1111/sjos.12611
J. Andrade
{"title":"On the robustness to outliers of the Student‐t process","authors":"J. Andrade","doi":"10.1111/sjos.12611","DOIUrl":"https://doi.org/10.1111/sjos.12611","url":null,"abstract":"The theory of Bayesian robustness modeling uses heavy‐tailed distributions to resolve conflicts of information by rejecting automatically the outlying information in favor of the other sources of information. In particular, the Student's‐t process is a natural alternative to the Gaussian process when the data might carry atypical information. Several works attest to the robustness of the Student t$$ t $$ process, however, the studies are mostly guided by intuition and focused mostly on the computational aspects rather than the mathematical properties of the involved distributions. This work uses the theory of regular variation to address the robustness of the Student t$$ t $$ process in the context of nonlinear regression, that is, the behavior of the posterior distribution in the presence of outliers in the inputs, in the outputs, or in both sources of information. In all these cases, under certain conditions, it is shown that the posterior distribution tends to a quantity that does not depend on the atypical information, then, for every case, the limiting posterior distribution as the outliers tend to infinity is provided. The impact of outliers on the predictive posterior distribution is also addressed. The theory is illustrated with a few simulated examples.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"725 - 749"},"PeriodicalIF":1.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47392318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Nonparametric asymptotic confidence intervals for extreme quantiles 极值分位数的非参数渐近置信区间
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-09-06 DOI: 10.1111/sjos.12610
L. Gardes, Samuel Maistre
{"title":"Nonparametric asymptotic confidence intervals for extreme quantiles","authors":"L. Gardes, Samuel Maistre","doi":"10.1111/sjos.12610","DOIUrl":"https://doi.org/10.1111/sjos.12610","url":null,"abstract":"In this paper, we propose new asymptotic confidence intervals for extreme quantiles, that is, for quantiles located outside the range of the available data. We restrict ourselves to the situation where the underlying distribution is heavy‐tailed. While asymptotic confidence intervals are mostly constructed around a pivotal quantity, we consider here an alternative approach based on the distribution of order statistics sampled from a uniform distribution. The convergence of the coverage probability to the nominal one is established under a classical second‐order condition. The finite sample behavior is also examined and our methodology is applied to a real dataset.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"825 - 841"},"PeriodicalIF":1.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47579507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A conversation with Elja Arjas (Helsinki, November 2021 and March 2022) 与Elja Arjas的对话(赫尔辛基，2021年11月和2022年3月)
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-09-05 DOI: 10.1111/sjos.12612
J. Corander
{"title":"A conversation with Elja Arjas (Helsinki, November 2021 and March 2022)","authors":"J. Corander","doi":"10.1111/sjos.12612","DOIUrl":"https://doi.org/10.1111/sjos.12612","url":null,"abstract":"Statistics as an independent scientific discipline is relatively young in Finland. Its active history stretches back roughly a century, with the past 50 years signifying a period of growth. Few other academics such as Elja Arjas, now professor emeritus at University of Helsinki, have played a prominent role in establishing statistics in Finland. This conversation tries to illuminate how this came to happen and what was needed to push statistics as a discipline to a firmer ground. We do not have a looking glass at our disposal but will nevertheless also try make some predictions about the future.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"12 - 3"},"PeriodicalIF":1.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44161588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Approximate exchangeability and de Finetti priors in 2022 2022年的近似交换性和de Finetti先验
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-08-29 DOI: 10.1111/sjos.12609
P. Diaconis
{"title":"Approximate exchangeability and de Finetti priors in 2022","authors":"P. Diaconis","doi":"10.1111/sjos.12609","DOIUrl":"https://doi.org/10.1111/sjos.12609","url":null,"abstract":"This is a review paper, beginning with de Finetti's work on partial exchangeability, continuing with his approach to approximate exchangeability, and then his (surprising) approach to assigning informative priors in nonstandard situations. Recent progress on Markov chain Monte Carlo methods for drawing conclusions is supplemented by a review of work by Gerencsér and Ottolini on getting honest bounds for rates of convergence. The paper concludes with a speculative approach to combining classical asymptotics with Monte Carlo. This promises real speed‐ups and makes a nice example of how theory and computation can interact.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"38 - 53"},"PeriodicalIF":1.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45814131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Consistent Bayesian information criterion based on a mixture prior for possibly high‐dimensional multivariate linear regression models 基于混合先验的可能高维多元线性回归模型的一致贝叶斯信息准则
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-08-19 DOI: 10.1111/sjos.12617
Haruki Kono, T. Kubokawa
{"title":"Consistent Bayesian information criterion based on a mixture prior for possibly high‐dimensional multivariate linear regression models","authors":"Haruki Kono, T. Kubokawa","doi":"10.1111/sjos.12617","DOIUrl":"https://doi.org/10.1111/sjos.12617","url":null,"abstract":"In the problem of selecting variables in a multivariate linear regression model, we derive new Bayesian information criteria based on a prior mixing a smooth distribution and a delta distribution. Each of them can be interpreted as a fusion of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Inheriting their asymptotic properties, our information criteria are consistent in variable selection in both the large‐sample and the high‐dimensional asymptotic frameworks. In numerical simulations, variable selection methods based on our information criteria choose the true set of variables with high probability in most cases.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"1022 - 1047"},"PeriodicalIF":1.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44697520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Large‐scale covariate‐assisted two‐sample inference under dependence 依赖条件下的大尺度协变量辅助双样本推理
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-08-18 DOI: 10.1111/sjos.12608
Pengfei Wang, Wensheng Zhu
{"title":"Large‐scale covariate‐assisted two‐sample inference under dependence","authors":"Pengfei Wang, Wensheng Zhu","doi":"10.1111/sjos.12608","DOIUrl":"https://doi.org/10.1111/sjos.12608","url":null,"abstract":"The problems of large‐scale two‐sample inference often arise from the statistical analysis of “high throughput\" data. Conventional multiple testing procedures usually suffer from loss of testing efficiency when conducting two‐sample t$$ t $$ ‐tests directly. To some extent, this is because of the ignorance of sparsity information. Moreover, the two‐sample tests commonly have local correlations, and neglecting the dependence structure may decrease the statistical accuracy. Therefore, it is imperative to develop a procedure that considers both sparsity information and dependence structure among the tests. We start by introducing a novel dependence model to allow for sparsity information and dependence structure. Based on the dependence model, we propose a covariate‐assisted local index of significance (COALIS)$$ left(mathbf{COALIS}right) $$ procedure and show that it is valid and optimal. Then a data‐driven procedure is developed to mimic the oracle procedure. Both simulations and real data analysis show that the COALIS procedure outperforms its competitors.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"49 1","pages":"1421 - 1447"},"PeriodicalIF":1.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47400582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","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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