Electronic Journal of Statistics最新文献_第9页

Testing subspace restrictions in the presence of high dimensional nuisance parameters 在存在高维干扰参数的情况下测试子空间限制

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2058

Alessio Sancetta

引用次数: 0

Concentration inequalities for non-causal random fields 非因果随机场的集中不等式

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs1992

Rémy Garnier, Raphael Langhendries

引用次数: 4

Estimation of the variance matrix in bivariate classical measurement error models 二元经典测量误差模型中方差矩阵的估计

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs1996

Elif Kekeç, I. Van Keilegom

{"title":"Estimation of the variance matrix in bivariate classical measurement error models","authors":"Elif Kekeç, I. Van Keilegom","doi":"10.1214/22-ejs1996","DOIUrl":"https://doi.org/10.1214/22-ejs1996","url":null,"abstract":": The presence of measurement errors is a ubiquitously faced problem and plenty of work has been done to overcome this when a single covariate is mismeasured under a variety of conditions. However, in practice, it is possible that more than one covariate is measured with error. When measurements are taken by the same device, the errors of these measurements are likely correlated. In this paper, we present a novel approach to estimate the covariance matrix of classical additive errors in the absence of validation data or auxiliary variables when two covariates are subject to measurement error. Our method assumes these errors to be following a bivariate normal distribution. We show that the variance matrix is identiﬁable under certain conditions on the support of the error-free variables and propose an estimation method based on an expansion of Bernstein polynomials. To investigate the per- formance of the proposed estimation method, the asymptotic properties of the estimator are examined and a diverse set of simulation studies is con- ducted. The estimated matrix is then used by the simulation-extrapolation (SIMEX) algorithm to reduce the bias caused by measurement error in lo- gistic regression models. Finally, the method is demonstrated using data from the Framingham Heart Study.","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":"47033673","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

Poisson mean vector estimation with nonparametric maximum likelihood estimation and application to protein domain data 非参数极大似然估计的泊松均值向量估计及其在蛋白质结构域数据中的应用

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2029

Hoyoung Park, Junyong Park

引用次数: 0

Estimating the number of communities by spectral methods 用谱方法估计群落数量

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1971

Can M. Le, E. Levina

引用次数: 14

Nested covariance functions on graphs with Euclidean edges cross time 具有欧几里得边的图上的嵌套协方差函数

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2039

E. Porcu, X. Emery, A. Peron

引用次数: 0

Robust consistent estimators for ROC curves with covariates 带有协变量的ROC曲线的稳健一致估计

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2042

Ana M. Bianco, G. Boente, W. González-Manteiga

引用次数: 1

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

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

Penalized nonparametric likelihood-based inference for current status data model 当前状态数据模型的基于似然的非参数惩罚推理

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/21-ejs1970

Meiling Hao, Yuanyuan Lin, Kin-Yat Liu, Xingqiu Zhao

引用次数: 0