Electronic Journal of Statistics最新文献_第9页

Minimal σ-field for flexible sufficient dimension reduction 最小的σ-域为灵活的充分降维

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs1999

Hanmin Guo, Lin Hou, Y. Zhu

{"title":"Minimal σ-field for flexible sufficient dimension reduction","authors":"Hanmin Guo, Lin Hou, Y. Zhu","doi":"10.1214/22-ejs1999","DOIUrl":"https://doi.org/10.1214/22-ejs1999","url":null,"abstract":"Sufficient Dimension Reduction (SDR) becomes an important tool for mitigating the curse of dimensionality in high dimensional regression analysis. Recently, Flexible SDR (FSDR) has been proposed to extend SDR by finding lower dimensional projections of transformed explanatory variables. The dimensions of the projections however cannot fully represent the extent of data reduction FSDR can achieve. As a consequence, optimality and other theoretical properties of FSDR are currently not well understood. In this article, we propose to use the σ-field associated with the projections, together with their dimensions to fully characterize FSDR, and refer to the σ-field as the FSDR σ-field. We further introduce the concept of minimal FSDR σ-field and consider FSDR projections with the minimal σfield optimal. Under some mild conditions, we show that the minimal FSDR σ-field exists, attaining the lowest dimensionality at the same time. To estimate the minimal FSDR σ-field, we propose a two-stage procedure called the Generalized Kernel Dimension Reduction (GKDR) method and partially establish its consistency property under weak conditions. Extensive simulation experiments demonstrate that the GKDRmethod can effectively find the minimal FSDR σ-field and outperform other existing methods. The application of GKDR to a real life air pollution data set sheds new light on the connections between atmospheric conditions and air quality. MSC2020 subject classifications: Primary 62B05; secondary 62J02.","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":"46459761","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

Estimation of partially conditional average treatment effect by double kernel-covariate balancing 用双核协变量平衡估计部分条件平均治疗效果

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2000

Jiayi Wang, R. K. Wong, Shu Yang, K. C. G. Chan

引用次数: 3

Isotonic regression for elicitable functionals and their Bayes risk 可引出泛函的同调回归及其Bayes风险

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2034

Anja Mühlemann, Johanna F. Ziegel

引用次数: 0

Regularized high dimension low tubal-rank tensor regression 正则化高维低管阶张量回归

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2004

S. Roy, G. Michailidis

{"title":"Regularized high dimension low tubal-rank tensor regression","authors":"S. Roy, G. Michailidis","doi":"10.1214/22-ejs2004","DOIUrl":"https://doi.org/10.1214/22-ejs2004","url":null,"abstract":": Tensor regression models are of emerging interest in diverse ﬁelds of social and behavioral sciences, including neuroimaging analysis, neural networks, image processing and so on. Recent theoretical advance- ments of tensor decomposition have facilitated signiﬁcant development of various tensor regression models. The focus of most of the available lit- erature has been on the Canonical Polyadic (CP) decomposition and its variants for the regression coeﬃcient tensor. A CP decomposed coeﬃcient tensor enables estimation with relatively small sample size, but it may not always capture the underlying complex structure in the data. In this work, we leverage the recently developed concept of tubal rank and develop a tensor regression model, wherein the coeﬃcient tensor is decomposed into two components: a low tubal rank tensor and a structured sparse one. We ﬁrst address the issue of identiﬁability of the two components comprising the coeﬃcient tensor and subsequently develop a fast and scalable Alternating Minimization algorithm to solve the convex regularized program. Further, we provide ﬁnite sample error bounds under high dimensional scaling for the model parameters. The performance of the model is assessed on synthetic data and is also used in an application involving data from an intelligent tutoring platform.","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":"46715850","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

LAMN property for multivariate inhomogeneous diffusions with discrete observations 离散观测下多元非齐次扩散的LAMN性质

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 DOI: 10.1214/22-ejs2049

N. Tran, H. Ngo

引用次数: 0

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

CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets. CDPA:高维数据集之间的共同和独特模式分析。

IF 1.1 4区数学

Electronic Journal of Statistics Pub Date : 2022-01-01 Epub Date: 2022-04-04 DOI: 10.1214/22-EJS2008

Hai Shu, Zhe Qu

{"title":"CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets.","authors":"Hai Shu, Zhe Qu","doi":"10.1214/22-EJS2008","DOIUrl":"https://doi.org/10.1214/22-EJS2008","url":null,"abstract":"<p><p>A representative model in integrative analysis of two high-dimensional correlated datasets is to decompose each data matrix into a low-rank common matrix generated by latent factors shared across datasets, a low-rank distinctive matrix corresponding to each dataset, and an additive noise matrix. Existing decomposition methods claim that their common matrices capture the common pattern of the two datasets. However, their so-called common pattern only denotes the common latent factors but ignores the common pattern between the two coefficient matrices of these common latent factors. We propose a new unsupervised learning method, called the common and distinctive pattern analysis (CDPA), which appropriately defines the two types of data patterns by further incorporating the common and distinctive patterns of the coefficient matrices. A consistent estimation approach is developed for high-dimensional settings, and shows reasonably good finite-sample performance in simulations. Our simulation studies and real data analysis corroborate that the proposed CDPA can provide better characterization of common and distinctive patterns and thereby benefit data mining.</p>","PeriodicalId":49272,"journal":{"name":"Electronic Journal of Statistics","volume":" ","pages":"2475-2517"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410619/pdf/nihms-1830529.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33443550","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: 1

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