发布求助
文献互助 智能选刊 最新文献

Statistical Papers最新文献

筛选
英文 中文
Hessian and increasing-Hessian orderings of multivariate skew-elliptical random vectors with applications in actuarial science 多变量偏斜椭圆随机向量的赫斯和递增赫斯排序及其在精算学中的应用
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-31 DOI: 10.1007/s00362-024-01580-y
Chuancun Yin, Jing Yao, Yang Yang
{"title":"Hessian and increasing-Hessian orderings of multivariate skew-elliptical random vectors with applications in actuarial science","authors":"Chuancun Yin, Jing Yao, Yang Yang","doi":"10.1007/s00362-024-01580-y","DOIUrl":"https://doi.org/10.1007/s00362-024-01580-y","url":null,"abstract":"<p>In this work, we establish some stochastic comparison results for multivariate skew-elliptical random vectors. These multivariate stochastic comparisons involve Hessian and increasing-Hessian orderings and many of their special cases. We provide necessary and/or sufficient conditions for the orderings by comparing the underlying model parameters. In addition, we investigate the (positive) linear forms of usual stochastic, convex and increasing convex, positive convex and increasing-positive-convex orderings. Using these theoretical results, we explore two applications. The first involves determining the upper bound of multivariate skew-elliptical risk variables under specific parameter constraints. The other one focuses on assessing the portfolio aggregation risks. Finally, two examples based on numerical simulations and real data from an Australian insurance company illustrate the established results and practical explanations.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"36 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Information matrix equivalence in the presence of censoring: a goodness-of-fit test for semiparametric copula models with multivariate survival data 存在删减时的信息矩阵等价性：多变量生存数据半参数共轭模型的拟合优度检验
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-31 DOI: 10.1007/s00362-024-01566-w
Qian M. Zhou
{"title":"Information matrix equivalence in the presence of censoring: a goodness-of-fit test for semiparametric copula models with multivariate survival data","authors":"Qian M. Zhou","doi":"10.1007/s00362-024-01566-w","DOIUrl":"https://doi.org/10.1007/s00362-024-01566-w","url":null,"abstract":"<p>Various goodness-of-fit tests are designed based on the so-called <i>information matrix equivalence</i>: if the assumed model is correctly specified, two information matrices that are derived from the likelihood function are equivalent. In the literature, this principle has been established for the likelihood function with fully observed data, but it has not been verified under the likelihood for censored data. In this manuscript, we prove the information matrix equivalence in the framework of semiparametric copula models for multivariate censored survival data. Based on this equivalence, we propose an information ratio (IR) test for the specification of the copula function. The IR statistic is constructed via comparing consistent estimates of the two information matrices. We derive the asymptotic distribution of the IR statistic and propose a parametric bootstrap procedure for the finite-sample <i>P</i>-value calculation. The performance of the IR test is investigated via a simulation study and a real data example.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"18 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Bernstein estimator for conditional copulas 伯恩斯坦条件共线估计器
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-31 DOI: 10.1007/s00362-024-01573-x
Noël Veraverbeke
{"title":"Bernstein estimator for conditional copulas","authors":"Noël Veraverbeke","doi":"10.1007/s00362-024-01573-x","DOIUrl":"https://doi.org/10.1007/s00362-024-01573-x","url":null,"abstract":"<p>The use of Bernstein polynomials in smooth nonparametric estimation of copulas has been well established in recent years. Their good properties in terms of bias and variance are well known. In this note we generalize some of the asymptotic theory to conditional copulas, that is where the dependence structure between the variables changes with a value of a random covariate. We obtain asymptotic representations and asymptotic normality for a conditional copula.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"56 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Dimension reduction-based adaptive-to-model semi-supervised classification 基于降维的自适应模型半监督分类法
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-30 DOI: 10.1007/s00362-024-01578-6
Xuehu Zhu, Rongzhu Zhao, Dan Zeng, Qian Zhao, Jun Zhang
{"title":"Dimension reduction-based adaptive-to-model semi-supervised classification","authors":"Xuehu Zhu, Rongzhu Zhao, Dan Zeng, Qian Zhao, Jun Zhang","doi":"10.1007/s00362-024-01578-6","DOIUrl":"https://doi.org/10.1007/s00362-024-01578-6","url":null,"abstract":"<p>This paper introduces a novel Dimension Reduction-based Adaptive-to-model Semi-supervised Classification method, specifically designed for scenarios where the number of unlabeled samples significantly exceeds that of labeled samples. Leveraging the strengths of sufficient dimension reduction and non-parametric interpolation, the method significantly amplifies the value derived from unlabeled samples, thus enhancing the precision of the classification model. An iterative version is also presented to extract further insights from the interpolated unlabeled samples. Theoretical analyses and numerical studies demonstrate substantial improvements in classifier accuracy, particularly in the context of model misspecified. The effectiveness of the proposed method in enhancing classification accuracy is further substantiated through two empirical analyses: credit card application evaluations and coronary heart disease diagnostic assessments.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"83 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
On weighted version of dynamic cumulative residual inaccuracy measure based on extropy 基于外熵的动态累积残差误差测量的加权版本
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-25 DOI: 10.1007/s00362-024-01568-8
Morteza Mohammadi, Majid Hashempour
{"title":"On weighted version of dynamic cumulative residual inaccuracy measure based on extropy","authors":"Morteza Mohammadi, Majid Hashempour","doi":"10.1007/s00362-024-01568-8","DOIUrl":"https://doi.org/10.1007/s00362-024-01568-8","url":null,"abstract":"<p>This paper introduces the concept of dynamic cumulative residual extropy inaccuracy (DCREI) by expanding on the existing dynamic cumulative residual extropy (DCRE) measure and proposes a weighted version of it. The paper then investigates a characterization problem for the proposed weighted dynamic extropy inaccuracy measure under the proportional hazard model and characterizes some well-known lifetime distributions using the weighted dynamic cumulative residual extropy inaccuracy (WDCREI) measure. Additionally, the study discusses the stochastic ordering of WDCREI and certain results based on it. Non-parametric estimations of the proposed measures based on kernel and empirical estimators are suggested. Results of a simulation study show that the kernel-based estimators perform better than the empirical-based estimator. Finally, applications of the proposed measures on model selection are provided.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"9 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
A non-classical parameterization for density estimation using sample moments 利用样本矩进行密度估计的非经典参数化方法
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-20 DOI: 10.1007/s00362-024-01563-z
Guangyu Wu, Anders Lindquist
{"title":"A non-classical parameterization for density estimation using sample moments","authors":"Guangyu Wu, Anders Lindquist","doi":"10.1007/s00362-024-01563-z","DOIUrl":"https://doi.org/10.1007/s00362-024-01563-z","url":null,"abstract":"<p>Probability density estimation is a core problem in statistics and data science. Moment methods are an important means of density estimation, but they are generally strongly dependent on the choice of feasible functions, which severely affects the performance. In this paper, we propose a non-classical parametrization for density estimation using sample moments, which does not require the choice of such functions. The parametrization is induced by the squared Hellinger distance, and the solution minimizing it, which is proved to exist and be unique subject to a simple prior that does not depend on data, and which can be obtained by convex optimization. Statistical properties of the density estimator, together with an asymptotic error upper bound, are proposed for the estimator by power moments. Simulation results validate the performance of the estimator by a comparison to several prevailing methods. The convergence rate of the proposed estimator is proved to be <span>(m^{-1/2})</span> (<i>m</i> being the number of data samples), which is the optimal convergence rate for parametric estimators and exceeds that of the nonparametric estimators. To the best of our knowledge, the proposed estimator is the first one in the literature for which the power moments up to an arbitrary even order exactly match the sample moments, while the true density is not assumed to fall within specific function classes.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"57 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Geometric infinitely divisible autoregressive models 几何无限可分自回归模型
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-20 DOI: 10.1007/s00362-024-01564-y
Monika S. Dhull, Arun Kumar
{"title":"Geometric infinitely divisible autoregressive models","authors":"Monika S. Dhull, Arun Kumar","doi":"10.1007/s00362-024-01564-y","DOIUrl":"https://doi.org/10.1007/s00362-024-01564-y","url":null,"abstract":"<p>In this article, we discuss some geometric infinitely divisible (gid) random variables using the Laplace exponents which are Bernstein functions and study their properties. The distributional properties and limiting behavior of the probability densities of these gid random variables at <span>(0^{+})</span> are studied. The autoregressive (AR) models with gid marginals are introduced. Further, the first order AR process is generalized to <i>k</i>th order AR process. We also provide the parameter estimation method based on conditional least square and method of moments for the introduced AR(1) process. We also apply the introduced AR(1) model with geometric inverse Gaussian marginals on the household energy usage data which provide a good fit as compared to normal AR(1) data.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"5 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141153300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Variation comparison between infinitely divisible distributions and the normal distribution 无限可分分布与正态分布的变异比较
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-10 DOI: 10.1007/s00362-024-01561-1
Ping Sun, Ze-Chun Hu, Wei Sun
{"title":"Variation comparison between infinitely divisible distributions and the normal distribution","authors":"Ping Sun, Ze-Chun Hu, Wei Sun","doi":"10.1007/s00362-024-01561-1","DOIUrl":"https://doi.org/10.1007/s00362-024-01561-1","url":null,"abstract":"<p>Let <i>X</i> be a random variable with finite second moment. We investigate the inequality: <span>(P{|X-textrm{E}[X]|le sqrt{textrm{Var}(X)}}ge P{|Z|le 1})</span>, where <i>Z</i> is a standard normal random variable. We prove that this inequality holds for many familiar infinitely divisible continuous distributions including the Laplace, Gumbel, Logistic, Pareto, infinitely divisible Weibull, Log-normal, Student’s <i>t</i> and Inverse Gaussian distributions. Numerical results are given to show that the inequality with continuity correction also holds for some infinitely divisible discrete distributions.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"3 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Multivariate stochastic comparisons of sequential order statistics with non-identical components 具有非相同成分的序列阶次统计的多变量随机比较
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-07 DOI: 10.1007/s00362-024-01558-w
Tanmay Sahoo, Nil Kamal Hazra, Narayanaswamy Balakrishnan
{"title":"Multivariate stochastic comparisons of sequential order statistics with non-identical components","authors":"Tanmay Sahoo, Nil Kamal Hazra, Narayanaswamy Balakrishnan","doi":"10.1007/s00362-024-01558-w","DOIUrl":"https://doi.org/10.1007/s00362-024-01558-w","url":null,"abstract":"<p>Sequential order statistics (SOS) are useful tools for modeling the lifetimes of systems wherein the failure of a component has a significant impact on the lifetimes of the remaining surviving components. The SOS model is a general model that contains most of the existing models for ordered random variables. In this paper, we consider the SOS model with non-identical components and then discuss various univariate and multivariate stochastic comparison results in both one-and two-sample scenarios.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"128 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140883338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
New copula families and mixing properties 新的共轭系和混合特性
IF 1.3 3区 数学
Statistical Papers Pub Date : 2024-05-06 DOI: 10.1007/s00362-024-01559-9
Martial Longla
{"title":"New copula families and mixing properties","authors":"Martial Longla","doi":"10.1007/s00362-024-01559-9","DOIUrl":"https://doi.org/10.1007/s00362-024-01559-9","url":null,"abstract":"<p>We characterize absolutely continuous symmetric copulas with square integrable densities in this paper. This characterization is used to create new copula families, that are perturbations of the independence copula. The full study of mixing properties of Markov chains generated by these copula families is conducted. An extension that includes the Farlie–Gumbel–Morgenstern family of copulas is proposed. We propose some examples of copulas that generate non-mixing Markov chains, but whose convex combinations generate <span>(psi )</span>-mixing Markov chains. Some general results on <span>(psi )</span>-mixing are given. The Spearman’s correlation <span>(rho _S)</span> and Kendall’s <span>(tau )</span> are provided for the created copula families. Some general remarks are provided for <span>(rho _S)</span> and <span>(tau )</span>. A central limit theorem is provided for parameter estimators in one example. A simulation study is conducted to support derived asymptotic distributions for some examples.</p>","PeriodicalId":51166,"journal":{"name":"Statistical Papers","volume":"32 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140883228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
首页 上一页 下一页 尾页
0
×
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
请完成安全验证×
相关产品
×
本文献相关产品
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信