Statistical Theory and Related Fields最新文献_第10页

Discussion on “on studying extreme values and systematic risks with nonlinear time series models and tail dependence measures” 关于“用非线性时间序列模型和尾部相关测度研究极值和系统风险”的讨论

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Statistical Theory and Related Fields Pub Date : 2021-01-02 DOI: 10.1080/24754269.2021.1895528

Wen Xu, Huixia Judy Wang

{"title":"Discussion on “on studying extreme values and systematic risks with nonlinear time series models and tail dependence measures”","authors":"Wen Xu, Huixia Judy Wang","doi":"10.1080/24754269.2021.1895528","DOIUrl":"https://doi.org/10.1080/24754269.2021.1895528","url":null,"abstract":"Extreme value theory provides essential mathematical foundations for modelling tail risks and has wide applications. The emerging of big and heterogeneous data calls for the development of new extreme value theory and methods. For studying high-dimensional extremes and extreme clusters in time series, an important problem is how to measure and test for tail dependence between random variables. Section 3.1 of Dr. Zhang’s paper discusses some newly proposed tail dependence measures. In the era of big data, a timely and challenging question is how to study data from heterogeneous populations, e.g. from different sources. Section 3.2 reviews some new developments of extreme value theory for maxima of maxima. The theory and methods in Sections 3.1 and 2.3 set the foundations for modelling extremes of multivariate and heterogeneous data, and we believe they have wide applicability. We will discuss two possible directions: (1) measuring and testing of partial tail dependence; (2) application of the extreme value theory for maxima of maxima in highdimensional inference.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"26 - 30"},"PeriodicalIF":0.5,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1895528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43522771","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

Covariance estimation via fiducial inference. 基于基准推理的协方差估计。

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Statistical Theory and Related Fields Pub Date : 2021-01-01 Epub Date: 2021-02-15 DOI: 10.1080/24754269.2021.1877950

W Jenny Shi, Jan Hannig, Randy C S Lai, Thomas C M Lee

引用次数: 5

On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures 用非线性时间序列模型和尾部相关测度研究极值和系统风险

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Statistical Theory and Related Fields Pub Date : 2020-12-23 DOI: 10.1080/24754269.2020.1856590

Zhengjun Zhang

引用次数: 15

New extreme value theory for maxima of maxima 极大值的极大值的新极值理论

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Statistical Theory and Related Fields Pub Date : 2020-12-20 DOI: 10.1080/24754269.2020.1846115

Wenzhi Cao, Zhengjun Zhang

{"title":"New extreme value theory for maxima of maxima","authors":"Wenzhi Cao, Zhengjun Zhang","doi":"10.1080/24754269.2020.1846115","DOIUrl":"https://doi.org/10.1080/24754269.2020.1846115","url":null,"abstract":"Although advanced statistical models have been proposed to fit complex data better, the advances of science and technology have generated more complex data, e.g., Big Data, in which existing probability theory and statistical models find their limitations. This work establishes probability foundations for studying extreme values of data generated from a mixture process with the mixture pattern depending on the sample length and data generating sources. In particular, we show that the limit distribution, termed as the accelerated max-stable distribution, of the maxima of maxima of sequences of random variables with the above mixture pattern is a product of three types of extreme value distributions. As a result, our theoretical results are more general than the classical extreme value theory and can be applicable to research problems related to Big Data. Examples are provided to give intuitions of the new distribution family. We also establish mixing conditions for a sequence of random variables to have the limit distributions. The results for the associated independent sequence and the maxima over arbitrary intervals are also developed. We use simulations to demonstrate the advantages of our newly established maxima of maxima extreme value theory.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"232 - 252"},"PeriodicalIF":0.5,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1846115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47671905","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: 11

Nonlinear prediction via Hermite transformation 基于Hermite变换的非线性预测

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Statistical Theory and Related Fields Pub Date : 2020-12-17 DOI: 10.1080/24754269.2020.1856589

T. McElroy, Srinjoy Das

引用次数: 0

Nonignorable item nonresponse in panel data 面板数据中不可忽略的无响应项

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Statistical Theory and Related Fields Pub Date : 2020-12-17 DOI: 10.1080/24754269.2020.1856591

Sijing Li, J. Shao

引用次数: 1

Research on three-step accelerated gradient algorithm in deep learning 深度学习中三步加速梯度算法的研究

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Statistical Theory and Related Fields Pub Date : 2020-11-23 DOI: 10.1080/24754269.2020.1846414

Yongqiang Lian, Yincai Tang, Shirong Zhou

{"title":"Research on three-step accelerated gradient algorithm in deep learning","authors":"Yongqiang Lian, Yincai Tang, Shirong Zhou","doi":"10.1080/24754269.2020.1846414","DOIUrl":"https://doi.org/10.1080/24754269.2020.1846414","url":null,"abstract":"Gradient descent (GD) algorithm is the widely used optimisation method in training machine learning and deep learning models. In this paper, based on GD, Polyak's momentum (PM), and Nesterov accelerated gradient (NAG), we give the convergence of the algorithms from an initial value to the optimal value of an objective function in simple quadratic form. Based on the convergence property of the quadratic function, two sister sequences of NAG's iteration and parallel tangent methods in neural networks, the three-step accelerated gradient (TAG) algorithm is proposed, which has three sequences other than two sister sequences. To illustrate the performance of this algorithm, we compare the proposed algorithm with the three other algorithms in quadratic function, high-dimensional quadratic functions, and nonquadratic function. Then we consider to combine the TAG algorithm to the backpropagation algorithm and the stochastic gradient descent algorithm in deep learning. For conveniently facilitate the proposed algorithms, we rewite the R package ‘neuralnet’ and extend it to ‘supneuralnet’. All kinds of deep learning algorithms in this paper are included in ‘supneuralnet’ package. Finally, we show our algorithms are superior to other algorithms in four case studies.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"40 - 57"},"PeriodicalIF":0.5,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1846414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44709557","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

Target toxicity design for phase I dose-finding I期剂量测定的靶毒性设计

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Statistical Theory and Related Fields Pub Date : 2020-08-13 DOI: 10.1080/24754269.2020.1800331

Wenchuan Guo, B. Zhong

引用次数: 0

Covariate balancing based on kernel density estimates for controlled experiments 基于核密度估计的受控实验协变量平衡

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Statistical Theory and Related Fields Pub Date : 2020-08-12 DOI: 10.1080/24754269.2021.1878742

Yiou Li, Lulu Kang, Xiao Huang

{"title":"Covariate balancing based on kernel density estimates for controlled experiments","authors":"Yiou Li, Lulu Kang, Xiao Huang","doi":"10.1080/24754269.2021.1878742","DOIUrl":"https://doi.org/10.1080/24754269.2021.1878742","url":null,"abstract":"ABSTRACT Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes. A completely randomised design is usually used to randomly assign treatment levels to experimental units. When covariates of the experimental units are available, the experimental design should achieve covariate balancing among the treatment groups, such that the statistical inference of the treatment effects is not confounded with any possible effects of covariates. However, covariate imbalance often exists, because the experiment is carried out based on a single realisation of the complete randomisation. It is more likely to occur and worsen when the size of the experimental units is small or moderate. In this paper, we introduce a new covariate balancing criterion, which measures the differences between kernel density estimates of the covariates of treatment groups. To achieve covariate balance before the treatments are randomly assigned, we partition the experimental units by minimising the criterion, then randomly assign the treatment levels to the partitioned groups. Through numerical examples, we show that the proposed partition approach can improve the accuracy of the difference-in-mean estimator and outperforms the complete randomisation and rerandomisation approaches.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"102 - 113"},"PeriodicalIF":0.5,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1878742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41800210","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: 2

A three-parameter logistic regression model 一个三参数逻辑回归模型

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Statistical Theory and Related Fields Pub Date : 2020-07-24 DOI: 10.1080/24754269.2020.1796098

Xiaoli Yu, Shaoting Li, Jiahua Chen

引用次数: 0