Electronic Journal of Statistics最新文献_第10页

Estimating individualized treatment rules for treatments with hierarchical structure 评估具有层次结构的治疗的个性化治疗规则

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1948

Yiwei Fan, Xiaoling Lu, Junlong Zhao, H. Fu, Yufeng Liu

{"title":"Estimating individualized treatment rules for treatments with hierarchical structure","authors":"Yiwei Fan, Xiaoling Lu, Junlong Zhao, H. Fu, Yufeng Liu","doi":"10.1214/21-ejs1948","DOIUrl":"https://doi.org/10.1214/21-ejs1948","url":null,"abstract":"Precision medicine is an increasingly important area of research. Due to the heterogeneity of individual characteristics, patients may respond differently to treatments. One of the most important goals for precision medicine is to develop individualized treatment rules (ITRs) involving patients’ characteristics directly. As an interesting topic in clinical research, many statistical methods have been developed in recent years to find optimal ITRs. For binary treatments, outcome weighted learning (OWL) was proposed to find a decision function of patient characteristics maximizing the expected clinical outcome. Treatments with hierarchical structure are commonly seen in practice. In hierarchical scenarios, how to estimate ITRs is still unclear. We propose a new framework named hierarchical outcome-weighted angle-based learning (HOAL) to estimate ITRs for treatments with hierarchical structure. Statistical properties including Fisher consistency and convergence rates of the proposed method are presented. Simulations and an application to a type 2 diabetes study under linear and nonlinear learning show the highly competitive performance of our proposed procedure in both numerical accuracy and computational efficiency.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43790857","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

Nonparametric regression in nonstandard spaces 非标准空间中的非参数回归

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2056

Christof Schötz

引用次数: 0

Casting vector time series: algorithms for forecasting, imputation, and signal extraction 铸造向量时间序列:算法预测，imputation，和信号提取

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2068

T. McElroy

引用次数: 0

Dimension independent excess risk by stochastic gradient descent 随机梯度下降的维数无关超额风险

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2055

X. Chen, Qiang Liu, Xin T. Tong

引用次数: 2

A general framework for tensor screening through smoothing 通过平滑进行张量筛选的一般框架

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1954

Keqian Min, Qing Mai

{"title":"A general framework for tensor screening through smoothing","authors":"Keqian Min, Qing Mai","doi":"10.1214/21-ejs1954","DOIUrl":"https://doi.org/10.1214/21-ejs1954","url":null,"abstract":"Screening is an important technique for analyzing high-dimensional data. Most screening tools have been developed for vectors and are marginal in the sense that each variable is evaluated individually at a time. Many multi-dimensional arrays (tensors) are generated nowadays. In addition to being high-dimensional, these data further have the tensor structure that should be exploited for more efficient analysis. Variables adjacent to each other in a tensor tend to be important or unimportant at the same time. Such information is ignored by marginal screening methods. In this article, we propose a general framework for tensor screening called smoothed tensor screening (STS). STS combines the strength of current marginal screening methods with tensor structural information by aggregating the information of its adjacent variables when evaluating one variable. STS is widely applicable since the statistical utility used in screening can be chosen based on the underlying model or data type of the responses and predictors. Moreover, we establish the SURE screening property for STS under mild conditions. Numerical studies demonstrate that STS has better performance than marginal screening methods. MSC2020 subject classifications: 62P10, 62F07.","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41439926","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

Penalized estimation of threshold auto-regressive models with many components and thresholds. 多成分多阈值阈值自回归模型的惩罚性估计

IF 1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 Epub Date: 2022-03-22 DOI: 10.1214/22-EJS1982

Kunhui Zhang, Abolfazl Safikhani, Alex Tank, Ali Shojaie

{"title":"Penalized estimation of threshold auto-regressive models with many components and thresholds.","authors":"Kunhui Zhang, Abolfazl Safikhani, Alex Tank, Ali Shojaie","doi":"10.1214/22-EJS1982","DOIUrl":"10.1214/22-EJS1982","url":null,"abstract":"<p><p>Thanks to their simplicity and interpretable structure, autoregressive processes are widely used to model time series data. However, many real time series data sets exhibit non-linear patterns, requiring nonlinear modeling. The threshold Auto-Regressive (TAR) process provides a family of non-linear auto-regressive time series models in which the process dynamics are specific step functions of a thresholding variable. While estimation and inference for low-dimensional TAR models have been investigated, high-dimensional TAR models have received less attention. In this article, we develop a new framework for estimating high-dimensional TAR models, and propose two different sparsity-inducing penalties. The first penalty corresponds to a natural extension of classical TAR model to high-dimensional settings, where the same threshold is enforced for all model parameters. Our second penalty develops a more flexible TAR model, where different thresholds are allowed for different auto-regressive coefficients. We show that both penalized estimation strategies can be utilized in a three-step procedure that consistently learns both the thresholds and the corresponding auto-regressive coefficients. However, our theoretical and empirical investigations show that the direct extension of the TAR model is not appropriate for high-dimensional settings and is better suited for moderate dimensions. In contrast, the more flexible extension of the TAR model leads to consistent estimation and superior empirical performance in high dimensions.</p>","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":"16 1","pages":"1891-1951"},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088520/pdf/nihms-1885625.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9851486","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

On sufficient variable screening using log odds ratio filter 利用对数比值比滤波器进行充分变量筛选

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1951

Baoying Yang, Wenbo Wu, Xiangrong Yin

引用次数: 0

Monte Carlo Markov chains constrained on graphs for a target with disconnected support 具有断开支持的目标在图上约束的蒙特卡罗马尔可夫链

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2043

R. Cerqueti, Emilio De Santis

引用次数: 0

The robust nearest shrunken centroids classifier for high-dimensional heavy-tailed data 高维重尾数据的鲁棒最近收缩质心分类器

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2022

Shaokang Ren, Qing Mai

引用次数: 0

Two-sample test for equal distributions in separate metric space: New maximum mean discrepancy based approaches 独立度量空间中相等分布的两样本检验：基于最大均值差异的新方法

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2033

Jin-Ting Zhang, Łukasz Smaga

引用次数: 1