Journal of Computational and Graphical Statistics最新文献_第6页

Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA. 经验贝叶斯协方差分解及稀疏主成分分析中多重调优问题的解决。

IF 1.6 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-05-29 DOI: 10.1080/10618600.2026.2653766

Joonsuk Kang, Matthew Stephens

{"title":"Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA.","authors":"Joonsuk Kang, Matthew Stephens","doi":"10.1080/10618600.2026.2653766","DOIUrl":"10.1080/10618600.2026.2653766","url":null,"abstract":"Sparse Principal Components Analysis (PCA) has been proposed as a way to improve both interpretability and reliability of PCA. However, use of sparse PCA in practice is hindered by the difficulty of tuning the multiple hyperparameters that control the sparsity of different PCs (the \"multiple tuning problem\", MTP). Here we present a solution to the MTP using Empirical Bayes methods. We first introduce a general formulation for penalized PCA of a data matrix <math><mi>X</mi></math> , which includes some existing sparse PCA methods as special cases. We show that this formulation also leads to a penalized decomposition of the covariance (or Gram) matrix, <math> <msup><mrow><mi>X</mi></mrow> <mrow><mi>T</mi></mrow> </msup> <mi>X</mi></math> . We introduce empirical Bayes versions of these penalized problems, in which the penalties are determined by prior distributions that are estimated from the data by maximum likelihood rather than cross-validation. The resulting \"Empirical Bayes Covariance Decomposition\" provides a principled and efficient solution to the MTP in sparse PCA, and one that can be immediately extended to incorporate other structural assumptions (e.g., non-negative PCA). We illustrate the effectiveness of this approach on both simulated and real data examples.","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13249372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217733","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

Joint Registration and Conformal Prediction for Partially Observed Functional Data. 部分观测功能数据的联合配准与保形预测。

IF 1.6 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-04-14 DOI: 10.1080/10618600.2026.2634823

Fangyi Wang, Sebastian Kurtek, Yuan Zhang

{"title":"Joint Registration and Conformal Prediction for Partially Observed Functional Data.","authors":"Fangyi Wang, Sebastian Kurtek, Yuan Zhang","doi":"10.1080/10618600.2026.2634823","DOIUrl":"10.1080/10618600.2026.2634823","url":null,"abstract":"Predicting missing segments in partially observed functions is challenging due to infinite-dimensionality, complex dependence within and across observations, and irregular noise. These challenges are further exacerbated by the existence of two distinct sources of variation in functional data, termed amplitude (variation along the <math><mi>y</mi></math> -axis) and phase (variation along the <math><mi>x</mi></math> -axis). While registration can disentangle them from complete functional data, the process is more difficult for partial observations. Thus, existing methods for functional data prediction often treat phase variation as negligible. Furthermore, they typically require precise model specifications and/or rely on computationally intensive tools, such as bootstrapping, to construct prediction intervals. We propose a unified registration and prediction approach for partially observed functions using conformal prediction. Our method integrates registration and prediction in one algorithm while ensuring exchangeability through carefully constructed predictor-response pairs. Using a neighborhood smoothing algorithm, the framework produces pointwise prediction bands with finite-sample marginal coverage guarantees under weak assumptions. The method is easy to implement, computationally efficient, and permits simple parallelization. Numerical studies and real-world data examples demonstrate the effectiveness and practical utility of our method. Supplementary materials for this article are available online.","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13160368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147929691","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

A Statistical Functional Approach to Estimating Common Treatment Effects in Causal Inference 估计因果推理中常见处理效果的统计函数方法

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-03-19 DOI: 10.1080/10618600.2026.2648597

Kuan-Hsun Wu, Li-Pang Chen

引用次数: 0

Intrinsic Gaussian Process Regression Modeling for Manifold-valued Response Variable 流形值响应变量的内禀高斯过程回归建模

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-03-17 DOI: 10.1080/10618600.2026.2646187

Xinyu Li, Zhanfeng Wang, Hao Ding, Jian Qing Shi

引用次数: 0

Cell-TRICC: a Model-Based Approach to Cellwise-Trimmed Co-Clustering Cell-TRICC：一种基于模型的细胞修剪共聚类方法

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-03-13 DOI: 10.1080/10618600.2026.2634828

Edoardo Fibbi, Domenico Perrotta, Francesca Torti, Stefan Van Aelst, Tim Verdonck

引用次数: 0

Reluctant Interaction Modeling in Generalized Linear Models 广义线性模型中的不情愿交互建模

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-03-11 DOI: 10.1080/10618600.2026.2638491

Hai Lu, Guo Yu

引用次数: 0

Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration 数据集成的概率联合和个体变异解释（ProJIVE）

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-03-11 DOI: 10.1080/10618600.2026.2639081

Raphiel J. Murden, Ganzhong Tian, Deqiang Qiu, Benjamin B. Risk

引用次数: 0

FDR control in feature screening for ultrahigh-dimensional right-censored data via data-driven threshold selection 基于数据驱动阈值选择的超高维右截尾数据特征筛选中的FDR控制

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-02-18 DOI: 10.1080/10618600.2026.2632808

Jing Zhang, Hengjian Cui

引用次数: 0

Scalable skewed Bayesian inference for latent Gaussian models using INLA and variational Bayes 基于INLA和变分贝叶斯的潜在高斯模型的可扩展偏斜贝叶斯推理

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-02-18 DOI: 10.1080/10618600.2026.2628274

Shourya Dutta, Janet van Niekerk, Håvard Rue

引用次数: 0

Bayesian Smoothing and Variable selection using Variational Automatic Relevance Determination 基于变分自动关联确定的贝叶斯平滑和变量选择

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-02-17 DOI: 10.1080/10618600.2026.2619519

Zihe Liu, Diptarka Saha, Feng Liang

引用次数: 0