Statistica Sinica最新文献_第8页

Semiparametric Estimation of Non-ignorable Missingness With Refreshment Sample 点心样本不可忽略缺失的半参数估计

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0214

Jianfei Zheng, Jing Wang, L. Xue, A. Qu

引用次数: 0

A More Efficient Isomorphism Check for Two-Level Nonregular Designs 两级不规则设计的一种更有效的同构检验

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0200

Chunyan Wang, Robert W. Mee

引用次数: 0

Sparse and Low-Rank Matrix Quantile Estimation With Application to Quadratic Regression 稀疏低秩矩阵分位数估计及其在二次回归中的应用

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202021.0140

Wenqi Lu, Zhongyi Zhu, Heng Lian

引用次数: 1

Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials with Time-to-event Outcomes under the AFT Model AFT模型下具有事件时间结果的协变量自适应随机临床试验的假设检验

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0011

Jianling Wang, Thuan Nguyen, Y. Luan, Jiming Jiang

引用次数: 0

Reinforcement Learning via Nonparametric Smoothing in a Continuous-Time Stochastic Setting with Noisy Data 基于非参数平滑的连续时间随机噪声环境下的强化学习

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0407

Chenyang Jiang, Bowen Hu, Yazhen Wang, Shang Wu

引用次数: 0

Hypotheses Testing of Functional Principal Components 功能主成分的假设检验

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0309

Zening Song, Lijian Yang, Yuanyuan Zhang

{"title":"Hypotheses Testing of Functional Principal Components","authors":"Zening Song, Lijian Yang, Yuanyuan Zhang","doi":"10.5705/ss.202022.0309","DOIUrl":"https://doi.org/10.5705/ss.202022.0309","url":null,"abstract":": We propose a test for the hypothesis that the standardized functional principal components (FPCs) of functional data are equal to a given set of orthonormal bases (e.g., the Fourier basis). Using estimates of individual trajectories that satisfy certain approximation conditions, we construct a chi-square-type statistic, and show that it is oracally e(cid:14)cient under the null hypothesis, in the sense that its limiting distribution is the same as that of an infeasible statistic using all trajectories, known as the oracle.\" The null limiting distribution is an in(cid:12)nite Gaussian quadratic form, and we obtain a consistent estimator of its quantile. A test statistic based on the chi-squared-type statistic and the approximate quantile of the Gaussian quadratic form is shown to be both of the nominal asymptotic signi(cid:12)cance level and asymptotically correct. It is further shown that B-spline trajectory estimates meet the required approximation conditions. Simulation studies demonstrate the superior (cid:12)nite-sample performance of the proposed testing procedure. Using electroencephalogram (EEG) data, the proposed procedure con(cid:12)rms an interesting discovery that the centered EEG data are generated from a small","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70940204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Two-Sample Tests for Relevant Differences in the Eigenfunctions of Covariance Operators 协方差算子特征函数相关差异的双样本检验

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0365

Alexander Aue, Holger Dette, Gregory Rice

{"title":"Two-Sample Tests for Relevant Differences in the Eigenfunctions of Covariance Operators","authors":"Alexander Aue, Holger Dette, Gregory Rice","doi":"10.5705/ss.202020.0365","DOIUrl":"https://doi.org/10.5705/ss.202020.0365","url":null,"abstract":"This paper deals with two-sample tests for functional time series data, which have become widely available in conjunction with the advent of modern complex observation systems. Here, particular interest is in evaluating whether two sets of functional time series observations share the shape of their primary modes of variation as encoded by the eigenfunctions of the respective covariance operators. To this end, a novel testing approach is introduced that connects with, and extends, existing literature in two main ways. First, tests are set up in the relevant testing framework, where interest is not in testing an exact null hypothesis but rather in detecting deviations deemed sufficiently relevant, with relevance determined by the practitioner and perhaps guided by domain experts. Second, the proposed test statistics rely on a self-normalization principle that helps to avoid the notoriously difficult task of estimating the long-run covariance structure of the underlying functional time series. The main theoretical result of this paper is the derivation of the large-sample behavior of the proposed test statistics. Empirical evidence, indicating that the proposed procedures work well in finite samples and compare favorably with competing methods, is provided through a simulation study, and an application to annual temperature data.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"111 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":"135181369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Consistency of BIC Model Averaging BIC模型平均的一致性

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202021.0145

Ze Chen, Jianqiang Zhang, Wangli Xu, Yuhong Yang

引用次数: 1

Hypothesis Testing in High-Dimensional Instrumental Variables Regression With an Application to Genomics Data 高维工具变量回归中的假设检验及其在基因组学数据中的应用

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202019.0408

Jiarui Lu, Hongzhe Li

{"title":"Hypothesis Testing in High-Dimensional Instrumental Variables Regression With an Application to Genomics Data","authors":"Jiarui Lu, Hongzhe Li","doi":"10.5705/ss.202019.0408","DOIUrl":"https://doi.org/10.5705/ss.202019.0408","url":null,"abstract":"Gene expression and phenotype association can be affected by potential unmeasured confounders from multiple sources, leading to biased estimates of the associations. Since genetic variants largely explain gene expression variations, they can be used as instruments in studying the association between gene expressions and phenotype in the framework of high dimensional instrumental variable (IV) regression. However, because the dimensions of both genetic variants and gene expressions are often larger than the sample size, statistical inferences such as hypothesis testing for such high dimensional IV models are not trivial and have not been investigated in literature. The problem is more challenging since the instrumental variables (e.g., genetic variants) have to be selected among a large set of genetic variants. This paper considers the problem of hypothesis testing for sparse IV regression models and presents methods for testing single regression coefficient and multiple testing of multiple coefficients, where the test statistic for each single coefficient is constructed based on an inverse regression. A multiple testing procedure is developed for selecting variables and is shown to control the false discovery rate. Simulations are conducted to evaluate the performance of our proposed methods. These methods are illustrated by an analysis of a yeast dataset in order to identify genes that are associated with growth in the presence of hydrogen peroxide.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"53 60 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":"135783365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Sparse Functional Principal Component Analysis in High Dimensions 高维稀疏泛函主成分分析

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0445

Xiaoyu Hu, Fang Yao

{"title":"Sparse Functional Principal Component Analysis in High Dimensions","authors":"Xiaoyu Hu, Fang Yao","doi":"10.5705/ss.202020.0445","DOIUrl":"https://doi.org/10.5705/ss.202020.0445","url":null,"abstract":"Functional principal component analysis (FPCA) is a fundamental tool and has attracted increasing attention in recent decades, while existing methods are restricted to data with a single or finite number of random functions (much smaller than the sample size $n$). In this work, we focus on high-dimensional functional processes where the number of random functions $p$ is comparable to, or even much larger than $n$. Such data are ubiquitous in various fields such as neuroimaging analysis, and cannot be properly modeled by existing methods. We propose a new algorithm, called sparse FPCA, which is able to model principal eigenfunctions effectively under sensible sparsity regimes. While sparsity assumptions are standard in multivariate statistics, they have not been investigated in the complex context where not only is $p$ large, but also each variable itself is an intrinsically infinite-dimensional process. The sparsity structure motivates a thresholding rule that is easy to compute without nonparametric smoothing by exploiting the relationship between univariate orthonormal basis expansions and multivariate Kahunen-Lo`eve (K-L) representations. We investigate the theoretical properties of the resulting estimators, and illustrate the performance with simulated and real data examples.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"12 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":"136092729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 3