Statistical Theory and Related Fields最新文献_第6页

Discussion on the paper ‘A review of distributed statistical inference’ 关于“分布式统计推断综述”一文的讨论

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Statistical Theory and Related Fields Pub Date : 2021-12-16 DOI: 10.1080/24754269.2021.2015861

Junlong Zhao

引用次数: 0

Eight predictive powers with historical and interim data for futility and efficacy analysis 具有历史和中期数据的八种预测能力，用于徒劳和疗效分析

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Statistical Theory and Related Fields Pub Date : 2021-10-25 DOI: 10.1080/24754269.2021.1991557

Ying-Ying Zhang, Tengzhong Rong, Man-Man Li

{"title":"Eight predictive powers with historical and interim data for futility and efficacy analysis","authors":"Ying-Ying Zhang, Tengzhong Rong, Man-Man Li","doi":"10.1080/24754269.2021.1991557","DOIUrl":"https://doi.org/10.1080/24754269.2021.1991557","url":null,"abstract":"ABSTRACT When the historical data of the early phase trial and the interim data of the Phase III trial are available, we should use them to give a more accurate prediction in both futility and efficacy analysis. The predictive power is an important measure of the practical utility of a proposed trial, and it is better than the classical statistical power in giving a good indication of the probability that the trial will demonstrate a positive or statistically significant outcome. In addition to the four predictive powers with historical and interim data available in the literature and summarized in Table 1, we discover and calculate another four predictive powers also summarized in Table 1, for one-sided hypotheses. Moreover, we calculate eight predictive powers summarized in Table 2, for the reversed hypotheses. The combination of the two tables gives us a complete picture of the predictive powers with historical and interim data for futility and efficacy analysis. Furthermore, the eight predictive powers with historical and interim data are utilized to guide the futility analysis in the tamoxifen example. Finally, extensive simulations have been conducted to investigate the sensitivity analysis of priors, sample sizes, interim result and interim time on different predictive powers.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"277 - 298"},"PeriodicalIF":0.5,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49302617","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: 1

High-dimensional proportionality test of two covariance matrices and its application to gene expression data 两个协方差矩阵的高维比例检验及其在基因表达数据中的应用

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Statistical Theory and Related Fields Pub Date : 2021-10-06 DOI: 10.1080/24754269.2021.1984373

Long Feng, Xiaoxu Zhang, Binghui Liu

{"title":"High-dimensional proportionality test of two covariance matrices and its application to gene expression data","authors":"Long Feng, Xiaoxu Zhang, Binghui Liu","doi":"10.1080/24754269.2021.1984373","DOIUrl":"https://doi.org/10.1080/24754269.2021.1984373","url":null,"abstract":"With the development of modern science and technology, more and more high-dimensional data appear in the application fields. Since the high dimension can potentially increase the complexity of the covariance structure, comparing the covariance matrices among populations is strongly motivated in high-dimensional data analysis. In this article, we consider the proportionality test of two high-dimensional covariance matrices, where the data dimension is potentially much larger than the sample sizes, or even larger than the squares of the sample sizes. We devise a novel high-dimensional spatial rank test that has much-improved power than many existing popular tests, especially for the data generated from some heavy-tailed distributions. The asymptotic normality of the proposed test statistics is established under the family of elliptically symmetric distributions, which is a more general distribution family than the normal distribution family, including numerous commonly used heavy-tailed distributions. Extensive numerical experiments demonstrate the superiority of the proposed test in terms of both empirical size and power. Then, a real data analysis demonstrates the practicability of the proposed test for high-dimensional gene expression data.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"161 - 174"},"PeriodicalIF":0.5,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43264799","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

Variable screening in multivariate linear regression with high-dimensional covariates 高维协变量多元线性回归的变量筛选

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Statistical Theory and Related Fields Pub Date : 2021-10-06 DOI: 10.1080/24754269.2021.1982607

Shiferaw B. Bizuayehu, Luquan Li, Jin Xu

引用次数: 2

An introduction to statistical learning with applications in R 统计学习在R中的应用介绍

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Statistical Theory and Related Fields Pub Date : 2021-09-26 DOI: 10.1080/24754269.2021.1980261

Fariha Sohil, Muhammad Umair Sohali, J. Shabbir

{"title":"An introduction to statistical learning with applications in R","authors":"Fariha Sohil, Muhammad Umair Sohali, J. Shabbir","doi":"10.1080/24754269.2021.1980261","DOIUrl":"https://doi.org/10.1080/24754269.2021.1980261","url":null,"abstract":"The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"87 - 87"},"PeriodicalIF":0.5,"publicationDate":"2021-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42115335","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: 2608

Exponential tilted likelihood for stationary time series models 平稳时间序列模型的指数倾斜似然

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Statistical Theory and Related Fields Pub Date : 2021-09-23 DOI: 10.1080/24754269.2021.1978207

Xiuzhen Zhang, Yukun Liu, Riquan Zhang, Zhiping Lu

{"title":"Exponential tilted likelihood for stationary time series models","authors":"Xiuzhen Zhang, Yukun Liu, Riquan Zhang, Zhiping Lu","doi":"10.1080/24754269.2021.1978207","DOIUrl":"https://doi.org/10.1080/24754269.2021.1978207","url":null,"abstract":"Depending on the asymptotical independence of periodograms, exponential tilted (ET) likelihood, as an effective nonparametric statistical method, is developed to deal with time series in this paper. Similar to empirical likelihood (EL), it still suffers from two drawbacks: the non-definition problem of the likelihood function and the under-coverage probability of confidence region. To overcome these two problems, we further proposed the adjusted ET (AET) likelihood. With a specific adjustment level, our simulation studies indicate that the AET method achieves a higher-order coverage precision than the unadjusted ET method. In addition, due to the good performance of ET under moment model misspecification [Schennach, S. M. (2007). Point estimation with exponentially tilted empirical likelihood. The Annals of Statistics, 35(2), 634–672. https://doi.org/10.1214/009053606000001208], we show that the one-order property of point estimate is preserved for the misspecified spectral estimating equations of the autoregressive coefficient of AR(1). The simulation results illustrate that the point estimates of the ET outperform those of the EL and their hybrid in terms of standard deviation. A real data set is analyzed for illustration purpose.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"254 - 263"},"PeriodicalIF":0.5,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44094075","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

Time-dependent reliability analysis for repairable consecutive-k-out-of-n:F system 可修连续n-:F系统的时变可靠性分析

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Statistical Theory and Related Fields Pub Date : 2021-09-13 DOI: 10.1080/24754269.2021.1971489

Gökhan Gökdere, H. K. Tony Ng

引用次数: 2

A review of distributed statistical inference 分布式统计推理综述

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Statistical Theory and Related Fields Pub Date : 2021-09-13 DOI: 10.1080/24754269.2021.1974158

Yuan Gao, Weidong Liu, Hansheng Wang, Xiaozhou Wang, Yibo Yan, Riquan Zhang

引用次数: 16

Interpreting uninterpretable predictors: kernel methods, Shtarkov solutions, and random forests 解释不可解释的预测因子:核方法、Shtarkov解和随机森林

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Statistical Theory and Related Fields Pub Date : 2021-09-08 DOI: 10.1080/24754269.2021.1974157

Tri Le, B. Clarke

引用次数: 1

On the non-local priors for sparsity selection in high-dimensional Gaussian DAG models 高维高斯DAG模型稀疏度选择的非局部先验

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Statistical Theory and Related Fields Pub Date : 2021-09-05 DOI: 10.1080/24754269.2021.1963182

Xuan Cao, F. Yang

引用次数: 0