Biometrika最新文献_第9页

A subsampling perspective for extending the validity of state-of-the-art bootstraps in the frequency domain 在频域扩展最先进自举的有效性的子采样视角

2区数学

Biometrika Pub Date : 2023-01-30 DOI: 10.1093/biomet/asad006

Haihan Yu, Mark S Kaiser, Daniel J Nordman

{"title":"A subsampling perspective for extending the validity of state-of-the-art bootstraps in the frequency domain","authors":"Haihan Yu, Mark S Kaiser, Daniel J Nordman","doi":"10.1093/biomet/asad006","DOIUrl":"https://doi.org/10.1093/biomet/asad006","url":null,"abstract":"Summary Bootstrapping spectral mean statistics has been a notoriously difficult problem over the past 25 years. Many frequency domain bootstraps are valid only for certain time series structures, e.g., linear processes, or for special types of statistics, i.e., ratio statistics, because such bootstraps fail to capture the limiting variance of spectral statistics in general settings. We address this issue with a different form of resampling, namely, subsampling. While not considered previously, subsampling provides consistent variance estimation under much weaker conditions than any existing bootstrap in the frequency domain. Mixing is not used, as is often standard with subsampling. Rather, subsampling can be generally justified under the same conditions needed for original spectral mean statistics to have distributional limits in the first place. This result has impacts for other bootstrap methods. Subsampling then applies to extending the validity of recent state-of-the-art bootstraps in the frequency domain. We nontrivially link subsampling to such bootstraps, which broadens their range, as moment and block assumptions needed for these are cut by more than half. Essentially, state-of-the-art bootstraps then require no more stringent assumptions than those needed for a target limit distribution to exist, which is unusual in the bootstrap world. We also close a gap in the theory of subsampling for time series with distributional approximations, in addition to variance estimation, for frequency domain statistics.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135554424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Correction to: Optimal row-column designs 更正:最佳行-列设计

IF 2.7 2区数学

Biometrika Pub Date : 2023-01-27 DOI: 10.1093/biomet/asad003

引用次数: 0

High Dimensional Analysis of Variance in Multivariate Linear Regression 多元线性回归的高维方差分析

IF 2.7 2区数学

Biometrika Pub Date : 2023-01-10 DOI: 10.1093/biomet/asad001

Zhipeng Lou, Xianyang Zhang, Weichi Wu

引用次数: 1

An instrumental variable method for point processes: generalized Wald estimation based on deconvolution 点过程的工具变量方法:基于反卷积的广义Wald估计

IF 2.7 2区数学

Biometrika Pub Date : 2023-01-09 DOI: 10.1093/biomet/asad005

Zhichao Jiang, Shizhe Chen, Peng Ding

引用次数: 4

Significance testing for canonical correlation analysis in high dimensions. 高维度典型相关分析的显著性检验。

IF 2.4 2区数学

Biometrika Pub Date : 2022-12-01 Epub Date: 2022-11-18 DOI: 10.1093/biomet/asab059

Ian W McKeague, Xin Zhang

引用次数: 0

Functional hybrid factor regression model for handling heterogeneity in imaging studies. 处理影像学研究异质性的功能混合因子回归模型。

IF 2.7 2区数学

Biometrika Pub Date : 2022-12-01 DOI: 10.1093/biomet/asac007

C Huang, H Zhu

引用次数: 2

A proximal distance algorithm for likelihood-based sparse covariance estimation. 基于似然法的稀疏协方差估计的近距离算法。

IF 2.7 2区数学

Biometrika Pub Date : 2022-12-01 Epub Date: 2022-02-16 DOI: 10.1093/biomet/asac011

Jason Xu, Kenneth Lange

{"title":"A proximal distance algorithm for likelihood-based sparse covariance estimation.","authors":"Jason Xu, Kenneth Lange","doi":"10.1093/biomet/asac011","DOIUrl":"10.1093/biomet/asac011","url":null,"abstract":"This paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are generated and solved via the proximal distance version of the majorization-minimization principle. The resulting algorithm executes rapidly, gracefully handles settings where the number of parameters exceeds the number of cases, yields a positive-definite solution, and enjoys desirable convergence properties. Empirically, we demonstrate that our approach outperforms competing methods across several metrics, for a suite of simulated experiments. Its merits are illustrated on international migration data and a case study on flow cytometry. Our findings suggest that the marginal and conditional dependency networks for the cell signalling data are more similar than previously concluded.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"1 1","pages":"1047-1066"},"PeriodicalIF":2.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10716840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60702732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Graphical Gaussian Process Models for Highly Multivariate Spatial Data. 高多元空间数据的图形高斯过程模型。

IF 2.7 2区数学

Biometrika Pub Date : 2022-12-01 DOI: 10.1093/biomet/asab061

Debangan Dey, Abhirup Datta, Sudipto Banerjee

{"title":"Graphical Gaussian Process Models for Highly Multivariate Spatial Data.","authors":"Debangan Dey, Abhirup Datta, Sudipto Banerjee","doi":"10.1093/biomet/asab061","DOIUrl":"https://doi.org/10.1093/biomet/asab061","url":null,"abstract":"For multivariate spatial Gaussian process (GP) models, customary specifications of cross-covariance functions do not exploit relational inter-variable graphs to ensure process-level conditional independence among the variables. This is undesirable, especially for highly multivariate settings, where popular cross-covariance functions such as the multivariate Matérn suffer from a \"curse of dimensionality\" as the number of parameters and floating point operations scale up in quadratic and cubic order, respectively, in the number of variables. We propose a class of multivariate \"Graphical Gaussian Processes\" using a general construction called \"stitching\" that crafts cross-covariance functions from graphs and ensures process-level conditional independence among variables. For the Matérn family of functions, stitching yields a multivariate GP whose univariate components are Matérn GPs, and conforms to process-level conditional independence as specified by the graphical model. For highly multivariate settings and decomposable graphical models, stitching offers massive computational gains and parameter dimension reduction. We demonstrate the utility of the graphical Matérn GP to jointly model highly multivariate spatial data using simulation examples and an application to air-pollution modelling.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"109 4","pages":"993-1014"},"PeriodicalIF":2.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838617/pdf/nihms-1786615.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9104899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Thresholded Graphical Lasso Adjusts for Latent Variables 阈值图形套索调整潜在变量

IF 2.7 2区数学

Biometrika Pub Date : 2022-11-10 DOI: 10.1093/biomet/asac060

Minjie Wang, Genevera I. Allen

引用次数: 4

Additive Models for Symmetric Positive-Definite Matrices and Lie Groups 对称正定矩阵与李群的加性模型

IF 2.7 2区数学

Biometrika Pub Date : 2022-09-29 DOI: 10.1093/biomet/asac055

Z. Lin, H. Müller, B. U. Park

引用次数: 8