Electronic Journal of Statistics最新文献_第2页

Subnetwork estimation for spatial autoregressive models in large-scale networks 大规模网络中空间自回归模型的子网络估计

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2139

Xuetong Li, Feifei Wang, Wei Lan, Hansheng Wang

{"title":"Subnetwork estimation for spatial autoregressive models in large-scale networks","authors":"Xuetong Li, Feifei Wang, Wei Lan, Hansheng Wang","doi":"10.1214/23-ejs2139","DOIUrl":"https://doi.org/10.1214/23-ejs2139","url":null,"abstract":"Large-scale networks are commonly encountered in practice (e.g., Facebook and Twitter) by researchers. In order to study the network interaction between different nodes of large-scale networks, the spatial autoregressive (SAR) model has been popularly employed. Despite its popularity, the estimation of a SAR model on large-scale networks remains very challenging. On the one hand, due to policy limitations or high collection costs, it is often impossible for independent researchers to observe or collect all network information. On the other hand, even if the entire network is accessible, estimating the SAR model using the quasi-maximum likelihood estimator (QMLE) could be computationally infeasible due to its high computational cost. To address these challenges, we propose here a subnetwork estimation method based on QMLE for the SAR model. By using appropriate sampling methods, a subnetwork, consisting of a much-reduced number of nodes, can be constructed. Subsequently, the standard QMLE can be computed by treating the sampled subnetwork as if it were the entire network. This leads to a significant reduction in information collection and model computation costs, which increases the practical feasibility of the effort. Theoretically, we show that the subnetwork-based QMLE is consistent and asymptotically normal under appropriate regularity conditions. Extensive simulation studies, based on both simulated and real network structures, are presented.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42334033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

On nonparametric estimation for cross-sectional sampled data under stationarity 平稳性下截面抽样数据的非参数估计

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2163

Kwun Chuen Gary Chan, Hok Kan Ling, Sheung Chi Phillip Yam

引用次数: 0

Envelopes and principal component regression 包络和主成分回归

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2154

Xin Zhang, Kai Deng, Qing Mai

{"title":"Envelopes and principal component regression","authors":"Xin Zhang, Kai Deng, Qing Mai","doi":"10.1214/23-ejs2154","DOIUrl":"https://doi.org/10.1214/23-ejs2154","url":null,"abstract":"Envelope methods offer targeted dimension reduction for various statistical models. The goal is to improve efficiency in multivariate parameter estimation by projecting the data onto a lower-dimensional subspace known as the envelope. Envelope approaches have advantages in analyzing data with highly correlated variables, but their iterative Grassmannian optimization algorithms do not scale very well with high-dimensional data. While the connections between envelopes and partial least squares in multivariate linear regression have promoted recent progress in high-dimensional studies of envelopes, we propose a more straightforward way of envelope modeling from a new principal component regression perspective. The proposed procedure, Non-Iterative Envelope Component Estimation (NIECE), has excellent computational advantages over the iterative Grassmannian optimization alternatives in high dimensions. We develop a unified theory that bridges the gap between envelope methods and principal components in regression. The new theoretical insights also shed light on the envelope subspace estimation error as a function of eigenvalue gaps of two symmetric positive definite matrices used in envelope modeling. We apply the new theory and algorithm to several envelope models, including response and predictor reduction in multivariate linear models, logistic regression, and Cox proportional hazard model. Simulations and illustrative data analysis show the potential for NIECE to improve standard methods in linear and generalized linear models significantly.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136207137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Bootstrap adjusted predictive classification for identification of subgroups with differential treatment effects under generalized linear models Bootstrap校正预测分类用于识别广义线性模型下具有差异治疗效果的亚组

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2108

Na Li, Yanglei Song, C. D. Lin, D. Tu

引用次数: 0

Variable selection for single-index varying-coefficients models with applications to synergistic G × E interactions. 单指标变系数模型的变量选择及其在协同G×E相互作用中的应用

IF 1.5 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 Epub Date: 2023-03-01 DOI: 10.1214/23-ejs2117

Shunjie Guan, Mingtao Zhao, Yuehua Cui

{"title":"Variable selection for single-index varying-coefficients models with applications to synergistic G × E interactions.","authors":"Shunjie Guan, Mingtao Zhao, Yuehua Cui","doi":"10.1214/23-ejs2117","DOIUrl":"10.1214/23-ejs2117","url":null,"abstract":"<p><p>Epidemiological evidence suggests that simultaneous exposures to multiple environmental risk factors (Es) can increase disease risk larger than the additive effect of individual exposure acting alone. The interaction between a gene and multiple Es on a disease risk is termed as synergistic gene-environment interactions (synG × E). Single-index varying-coefficients models (SIVCM) have been a promising tool to model synergistic G × E effect and to understand how multiple Es jointly influence genetic risks on a disease outcome. In this work, we proposed a unified variable selection approach for SIVCM to estimate different effects of gene variables: varying, non-zero constant and zero effects which respectively correspond to nonlinear synG × E, no synG × E and no genetic effect. For multiple environmental exposure variables, we also estimated and selected important environmental variables that contribute to the synergistic interaction effect. We theoretically evaluated the oracle property of the proposed variable selection approach. Extensive simulation studies were conducted to evaluate the finite sample performance of the method, considering both continuous and discrete gene variables. Application to a real dataset further demonstrated the utility of the method. Our method has broad applications in areas where the purpose is to identify synergistic interaction effect.</p>","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":"823-857"},"PeriodicalIF":1.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13247678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42849077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Characterization of the solutions set of the generalized LASSO problems for non-full rank cases 非满秩情况下广义LASSO问题解集的刻画

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2138

Jaesung Hwang, Joong-Yeon Won, Yongdai Kim

引用次数: 0

Tests for high-dimensional single-index models 高维单索引模型的测试

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2109

Leheng Cai, Xu Guo, Gaorong Li, Falong Tan

引用次数: 1

Estimating causal effects with hidden confounding using instrumental variables and environments 使用工具变量和环境估计隐含混淆的因果效应

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2160

James P. Long, Hongxu Zhu, Kim-Anh Do, Min Jin Ha

引用次数: 0

Asymptotic analysis of ML-covariance parameter estimators based on covariance approximations 基于协方差近似的ml -协方差参数估计的渐近分析

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2170

Reinhard Furrer, Michael Hediger

引用次数: 0

High-dimensional composite quantile regression: Optimal statistical guarantees and fast algorithms 高维复合分位数回归:最优统计保证和快速算法

4区数学

Electronic Journal of Statistics Pub Date : 2023-01-01 DOI: 10.1214/23-ejs2147

Haeseong Moon, Wen-Xin Zhou

{"title":"High-dimensional composite quantile regression: Optimal statistical guarantees and fast algorithms","authors":"Haeseong Moon, Wen-Xin Zhou","doi":"10.1214/23-ejs2147","DOIUrl":"https://doi.org/10.1214/23-ejs2147","url":null,"abstract":"The composite quantile regression (CQR) was introduced by Zou and Yuan [Ann. Statist. 36 (2008) 1108–1126] as a robust regression method for linear models with heavy-tailed errors while achieving high efficiency. Its penalized counterpart for high-dimensional sparse models was recently studied in Gu and Zou [IEEE Trans. Inf. Theory 66 (2020) 7132–7154], along with a specialized optimization algorithm based on the alternating direct method of multipliers (ADMM). Compared to the various first-order algorithms for penalized least squares, ADMM-based algorithms are not well-adapted to large-scale problems. To overcome this computational hardness, in this paper we employ a convolution-smoothed technique to CQR, complemented with iteratively reweighted ℓ1-regularization. The smoothed composite loss function is convex, twice continuously differentiable, and locally strong convex with high probability. We propose a gradient-based algorithm for penalized smoothed CQR via a variant of the majorize-minimization principal, which gains substantial computational efficiency over ADMM. Theoretically, we show that the iteratively reweighted ℓ1-penalized smoothed CQR estimator achieves near-minimax optimal convergence rate under heavy-tailed errors without any moment constraint, and further achieves near-oracle convergence rate under a weaker minimum signal strength condition than needed in Gu and Zou (2020). Numerical studies demonstrate that the proposed method exhibits significant computational advantages without compromising statistical performance compared to two state-of-the-art methods that achieve robustness and high efficiency simultaneously.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135911568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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