Biometrika最新文献_第9页

√2-Estimation for Smooth Eigenvectors of Matrix-Valued Functions √2-矩阵值函数光滑特征向量的估计

IF 2.7 2区数学

Biometrika Pub Date : 2023-03-15 DOI: 10.1093/biomet/asad018

Giovanni Motta, W. Wu, M. Pourahmadi

引用次数: 0

Testing generalized linear models with high-dimensional nuisance parameter. 测试具有高维滋扰参数的广义线性模型

IF 2.7 2区数学

Biometrika Pub Date : 2023-03-01 Epub Date: 2022-04-05 DOI: 10.1093/biomet/asac021

Jinsong Chen, Quefeng Li, Hua Yun Chen

{"title":"Testing generalized linear models with high-dimensional nuisance parameter.","authors":"Jinsong Chen, Quefeng Li, Hua Yun Chen","doi":"10.1093/biomet/asac021","DOIUrl":"10.1093/biomet/asac021","url":null,"abstract":"<p><p>Generalized linear models often have a high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional sub-vector of the model's coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and thus are computationally expensive. Here, we propose a computationally efficient test with a closed-form limiting distribution, which allows the parameter being tested to be either sparse or dense. We show that under certain regularity conditions, the type I error of the proposed method is asymptotically correct, and we establish its power under high-dimensional alternatives. Extensive simulations demonstrate the good performance of the proposed test and its robustness when certain sparsity assumptions are violated. We also apply the proposed method to Chinese famine sample data in order to show its performance when testing the significance of gene-environment interactions.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10800040","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: 0

Nonparametric estimation of the intensity function of a spatial point process on a Riemannian manifold 黎曼流形上空间点过程强度函数的非参数估计

2区数学

Biometrika Pub Date : 2023-02-28 DOI: 10.1093/biomet/asad012

S Ward, H S Battey, E A K Cohen

引用次数: 0

Equivariant Estimation of Fréchet Means 方差均值的等变估计

IF 2.7 2区数学

Biometrika Pub Date : 2023-02-24 DOI: 10.1093/biomet/asad014

A. Mccormack, P. Hoff

{"title":"Equivariant Estimation of Fréchet Means","authors":"A. Mccormack, P. Hoff","doi":"10.1093/biomet/asad014","DOIUrl":"https://doi.org/10.1093/biomet/asad014","url":null,"abstract":"\u0000 The Fréchet mean generalizes the concept of a mean to a metric space setting. In this work we consider equivariant estimation of Fréchet means for parametric models on metric spaces that are Riemannian manifolds. The geometry and symmetry of such a space is partially encoded by its isometry group of distance preserving transformations. Estimators that are equivariant under the isometry group take into account the symmetry of the metric space. For some models there exists an optimal equivariant estimator, which necessarily will perform as well or better than other common equivariant estimators, such as the maximum likelihood estimator or the sample Fréchet mean. We derive the general form of this minimum risk equivariant estimator and in a few cases provide explicit expressions for it. A result for finding the Fréchet mean for distributions with radially decreasing densities is presented and used to find expressions for the minimum risk equivariant estimator. In some models the isometry group is not large enough relative to the parametric family of distributions for there to exist a minimum risk equivariant estimator. In such cases, we introduce an adaptive equivariant estimator that uses the data to select a submodel for which there is a minimum risk equivariant estimator. Simulation results show that the adaptive equivariant estimator performs favourably relative to alternative estimators.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41441805","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: 1

Statistical inference for streamed longitudinal data 流式纵向数据的统计推断

2区数学

Biometrika Pub Date : 2023-02-20 DOI: 10.1093/biomet/asad010

Lan Luo, Jingshen Wang, Emily C Hector

{"title":"Statistical inference for streamed longitudinal data","authors":"Lan Luo, Jingshen Wang, Emily C Hector","doi":"10.1093/biomet/asad010","DOIUrl":"https://doi.org/10.1093/biomet/asad010","url":null,"abstract":"Summary Modern longitudinal data, for example from wearable devices, may consist of measurements of biological signals on a fixed set of participants at a diverging number of time-points. Traditional statistical methods are not equipped to handle the computational burden of repeatedly analysing the cumulatively growing dataset each time new data are collected. We propose a new estimation and inference framework for dynamic updating of point estimates and their standard errors along sequentially collected datasets with dependence, both within and between the datasets. The key technique is a decomposition of the extended inference function vector of the quadratic inference function constructed over the cumulative longitudinal data into a sum of summary statistics over data batches. We show how this sum can be recursively updated without the need to access the whole dataset, resulting in a computationally efficient streaming procedure with minimal loss of statistical efficiency. We prove consistency and asymptotic normality of our streaming estimator as the number of data batches diverges, even as the number of independent participants remains fixed. Simulations demonstrate the advantages of our approach over traditional statistical methods that assume independence between data batches. Finally, we investigate the relationship between physical activity and several diseases through analysis of accelerometry data from the National Health and Nutrition Examination Survey.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134905480","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

Sampling distribution for single-regression Granger causality estimators 单回归格兰杰因果估计量的抽样分布

2区数学

Biometrika Pub Date : 2023-02-14 DOI: 10.1093/biomet/asad009

A J Gutknecht, L Barnett

引用次数: 1

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":null,"pages":null},"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