Journal of the Royal Statistical Society Series B-Statistical Methodology最新文献

Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease. 对随机非线性域支持的功能数据进行可解释的判别分析，并应用于阿尔茨海默病。

IF 3.1 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2024-03-22 eCollection Date: 2024-09-01 DOI: 10.1093/jrsssb/qkae023

Eardi Lila, Wenbo Zhang, Swati Rane Levendovszky

{"title":"Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease.","authors":"Eardi Lila, Wenbo Zhang, Swati Rane Levendovszky","doi":"10.1093/jrsssb/qkae023","DOIUrl":"10.1093/jrsssb/qkae023","url":null,"abstract":"We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estimation of the most discriminant direction while controlling for its complexity with appropriate differential regularization. Our approach does not require prior estimation of the covariance structure of the functional predictors, which is computationally prohibitive in our application setting. We provide a theoretical analysis of the out-of-sample prediction error of the proposed model and explore the finite sample performance in a simulation setting. We apply the proposed method to a pooled dataset from Alzheimer's Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative. Through this application, we identify discriminant directions that capture both cortical geometric and thickness predictive features of Alzheimer's disease that are consistent with the existing neuroscience literature.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

GENIUS-MAWII: for robust Mendelian randomization with many weak invalid instruments. GENIUS-MAWII：用于有许多弱无效工具的稳健孟德尔随机化。

IF 3.1 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2024-03-14 eCollection Date: 2024-09-01 DOI: 10.1093/jrsssb/qkae024

Ting Ye, Zhonghua Liu, Baoluo Sun, Eric Tchetgen Tchetgen

引用次数: 0

Doubly robust calibration of prediction sets under covariate shift. 协变量偏移下预测集的双稳健校准。

IF 3.1 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2024-03-04 eCollection Date: 2024-09-01 DOI: 10.1093/jrsssb/qkae009

Yachong Yang, Arun Kumar Kuchibhotla, Eric Tchetgen Tchetgen

引用次数: 0

Gradient synchronization for multivariate functional data, with application to brain connectivity. 多变量功能数据的梯度同步，并应用于大脑连接。

IF 3.1 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2024-01-22 eCollection Date: 2024-07-01 DOI: 10.1093/jrsssb/qkad140

Yaqing Chen, Shu-Chin Lin, Yang Zhou, Owen Carmichael, Hans-Georg Müller, Jane-Ling Wang

{"title":"Gradient synchronization for multivariate functional data, with application to brain connectivity.","authors":"Yaqing Chen, Shu-Chin Lin, Yang Zhou, Owen Carmichael, Hans-Georg Müller, Jane-Ling Wang","doi":"10.1093/jrsssb/qkad140","DOIUrl":"10.1093/jrsssb/qkad140","url":null,"abstract":"Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11239314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding. 利用双重负向控制对未测量的网络干扰进行因果同伴效应的识别和估计。

IF 5.8 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-12-15 eCollection Date: 2024-04-01 DOI: 10.1093/jrsssb/qkad132

Naoki Egami, Eric J Tchetgen Tchetgen

引用次数: 0

Controlling the false discovery rate in transformational sparsity: Split Knockoffs 转换稀疏性中的错误发现率控制:拆分仿冒

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-11-14 DOI: 10.1093/jrsssb/qkad126

Yang Cao, Xinwei Sun, Yuan Yao

{"title":"Controlling the false discovery rate in transformational sparsity: Split Knockoffs","authors":"Yang Cao, Xinwei Sun, Yuan Yao","doi":"10.1093/jrsssb/qkad126","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad126","url":null,"abstract":"Abstract Controlling the False Discovery Rate (FDR) in a variable selection procedure is critical for reproducible discoveries, and it has been extensively studied in sparse linear models. However, it remains largely open in scenarios where the sparsity constraint is not directly imposed on the parameters but on a linear transformation of the parameters to be estimated. Examples of such scenarios include total variations, wavelet transforms, fused LASSO, and trend filtering. In this paper, we propose a data-adaptive FDR control method, called the Split Knockoff method, for this transformational sparsity setting. The proposed method exploits both variable and data splitting. The linear transformation constraint is relaxed to its Euclidean proximity in a lifted parameter space, which yields an orthogonal design that enables the orthogonal Split Knockoff construction. To overcome the challenge that exchangeability fails due to the heterogeneous noise brought by the transformation, new inverse supermartingale structures are developed via data splitting for provable FDR control without sacrificing power. Simulation experiments demonstrate that the proposed methodology achieves the desired FDR and power. We also provide an application to Alzheimer’s Disease study, where atrophy brain regions and their abnormal connections can be discovered based on a structural Magnetic Resonance Imaging dataset.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 2

Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis 中介路径分析中复合零假设的自适应自举检验

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-11-14 DOI: 10.1093/jrsssb/qkad129

Yinqiu He, Peter X K Song, Gongjun Xu

引用次数: 1

Stratification pattern enumerator and its applications 分层模式枚举器及其应用

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-11-14 DOI: 10.1093/jrsssb/qkad125

Ye Tian, Hongquan Xu

{"title":"Stratification pattern enumerator and its applications","authors":"Ye Tian, Hongquan Xu","doi":"10.1093/jrsssb/qkad125","DOIUrl":"https://doi.org/10.1093/jrsssb/qkad125","url":null,"abstract":"Abstract Space-filling designs are widely used in computer experiments. A minimum aberration-type space-filling criterion was recently proposed to rank and assess a family of space-filling designs including orthogonal array-based Latin hypercubes and strong orthogonal arrays. However, it is difficult to apply the criterion in practice because it requires intensive computation for determining the space-filling pattern, which measures the stratification properties of designs on various subregions. In this article, we propose a stratification pattern enumerator to characterise the stratification properties. The enumerator is easy to compute and can efficiently rank space-filling designs. We show that the stratification pattern enumerator is a linear combination of the space-filling pattern. Based on the connection, we develop efficient algorithms for calculating the space-filling pattern. In addition, we establish a lower bound for the stratification pattern enumerator and present construction methods for designs that achieve the lower bound using multiplication tables over Galois fields. The constructed designs have good space-filling properties in low-dimensional projections and are robust under various criteria.","PeriodicalId":49982,"journal":{"name":"Journal of the Royal Statistical Society Series B-Statistical Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Proposer of the vote of thanks to Waudby-Smith and Ramdas and contribution to the Discussion of “Estimating the means of bounded random variables by betting” 对Waudby-Smith和Ramdas的感谢以及对“通过投注估计有界随机变量的均值”的讨论的贡献的提案者

1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-11-09 DOI: 10.1093/jrsssb/qkad128

Peter Gürnwald

引用次数: 0

Image response regression via deep neural networks. 通过深度神经网络进行图像响应回归。

IF 3.1 1区数学

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2023-11-01 Epub Date: 2023-07-24 DOI: 10.1093/jrsssb/qkad073

Daiwei Zhang, Lexin Li, Chandra Sripada, Jian Kang

引用次数: 0