Statistica Sinica最新文献_第5页

On Block Cholesky Decomposition for Sparse Inverse Covariance Estimation 稀疏逆协方差估计的块Cholesky分解

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-08-18 DOI: 10.5705/ss.202023.0065

Xiaoning Kang, J. Lian, Xinwei Deng

引用次数: 0

A Data Fusion Method for Quantile Treatment Effects 一种分位数处理效果的数据融合方法

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-07-16 DOI: 10.5705/ss.202022.0288

Yijiao Zhang, Zhongyi Zhu

引用次数: 0

PARTIALLY FUNCTIONAL LINEAR QUANTILE REGRESSION WITH MEASUREMENT ERRORS. 有测量误差的部分函数线性量回归。

IF 1.5 3区数学

Statistica Sinica Pub Date : 2023-07-01 DOI: 10.5705/ss.202021.0246

Mengli Zhang, Lan Xue, Carmen D Tekwe, Yang Bai, Annie Qu

引用次数: 0

A Comparison of Estimators of Mean and Its Functions in Finite Populations 有限总体中均值及其函数估计量的比较

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-05-24 DOI: 10.5705/ss.202022.0181

Anurag Dey, P. Chaudhuri

{"title":"A Comparison of Estimators of Mean and Its Functions in Finite Populations","authors":"Anurag Dey, P. Chaudhuri","doi":"10.5705/ss.202022.0181","DOIUrl":"https://doi.org/10.5705/ss.202022.0181","url":null,"abstract":"Several well known estimators of finite population mean and its functions are investigated under some standard sampling designs. Such functions of mean include the variance, the correlation coefficient and the regression coefficient in the population as special cases. We compare the performance of these estimators under different sampling designs based on their asymptotic distributions. Equivalence classes of estimators under different sampling designs are constructed so that estimators in the same class have equivalent performance in terms of asymptotic mean squared errors (MSEs). Estimators in different equivalence classes are then compared under some superpopulations satisfying linear models. It is shown that the pseudo empirical likelihood (PEML) estimator of the population mean under simple random sampling without replacement (SRSWOR) has the lowest asymptotic MSE among all the estimators under different sampling designs considered in this paper. It is also shown that for the variance, the correlation coefficient and the regression coefficient of the population, the plug-in estimators based on the PEML estimator have the lowest asymptotic MSEs among all the estimators considered in this paper under SRSWOR. On the other hand, for any high entropy $pi$PS (HE$pi$PS) sampling design, which uses the auxiliary information, the plug-in estimators of those parameters based on the H'ajek estimator have the lowest asymptotic MSEs among all the estimators considered in this paper.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47403235","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

An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis. 一种高效的高维线性判别分析贪心搜索算法。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-05-01 DOI: 10.5705/ss.202021.0028

Hannan Yang, D Y Lin, Quefeng Li

{"title":"An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis.","authors":"Hannan Yang, D Y Lin, Quefeng Li","doi":"10.5705/ss.202021.0028","DOIUrl":"https://doi.org/10.5705/ss.202021.0028","url":null,"abstract":"High-dimensional classification is an important statistical problem that has applications in many areas. One widely used classifier is the Linear Discriminant Analysis (LDA). In recent years, many regularized LDA classifiers have been proposed to solve the problem of high-dimensional classification. However, these methods rely on inverting a large matrix or solving large-scale optimization problems to render classification rules-methods that are computationally prohibitive when the dimension is ultra-high. With the emergence of big data, it is increasingly important to develop more efficient algorithms to solve the high-dimensional LDA problem. In this paper, we propose an efficient greedy search algorithm that depends solely on closed-form formulae to learn a high-dimensional LDA rule. We establish theoretical guarantee of its statistical properties in terms of variable selection and error rate consistency; in addition, we provide an explicit interpretation of the extra information brought by an additional feature in a LDA problem under some mild distributional assumptions. We demonstrate that this new algorithm drastically improves computational speed compared with other high-dimensional LDA methods, while maintaining comparable or even better classification performance.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348717/pdf/nihms-1764480.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9847026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Marginal Bayesian Posterior Inference using Recurrent Neural Networks with Application to Sequential Models. 递归神经网络的边际贝叶斯后验推理及其在序列模型中的应用。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-05-01 DOI: 10.5705/ss.202020.0348

Thayer Fisher, Alex Luedtke, Marco Carone, Noah Simon

引用次数: 0

Globally Adaptive Longitudinal Quantile Regression with High Dimensional Compositional Covariates. 高维组成协变量的全局自适应纵向分位数回归。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-05-01 DOI: 10.5705/ss.202021.0006

Huijuan Ma, Qi Zheng, Zhumin Zhang, Huichuan Lai, Limin Peng

{"title":"Globally Adaptive Longitudinal Quantile Regression with High Dimensional Compositional Covariates.","authors":"Huijuan Ma, Qi Zheng, Zhumin Zhang, Huichuan Lai, Limin Peng","doi":"10.5705/ss.202021.0006","DOIUrl":"https://doi.org/10.5705/ss.202021.0006","url":null,"abstract":"In this work, we propose a longitudinal quantile regression framework that enables a robust characterization of heterogeneous covariate-response associations in the presence of high-dimensional compositional covariates and repeated measurements of both response and covariates. We develop a globally adaptive penalization procedure, which can consistently identify covariate sparsity patterns across a continuum set of quantile levels. The proposed estimation procedure properly aggregates longitudinal observations over time, and ensures the satisfaction of the sum-zero coefficient constraint that is needed for proper interpretation of the effects of compositional covariates. We establish the oracle rate of uniform convergence and weak convergence of the resulting estimators, and further justify the proposed uniform selector of the tuning parameter in terms of achieving global model selection consistency. We derive an efficient algorithm by incorporating existing R packages to facilitate stable and fast computation. Our extensive simulation studies confirm the theoretical findings. We apply the proposed method to a longitudinal study of cystic fibrosis children where the association between gut microbiome and other diet-related biomarkers is of interest.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361693/pdf/nihms-1757788.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9862958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Subsampling and Jackknifing: A Practically Convenient Solution for Large Data Analysis With Limited Computational Resources 子采样和折刀:计算资源有限的大数据分析的一种实用方便的解决方案

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-04-13 DOI: 10.5705/ss.202021.0257

Shuyuan Wu, Xuening Zhu, Hansheng Wang

{"title":"Subsampling and Jackknifing: A Practically Convenient Solution for Large Data Analysis With Limited Computational Resources","authors":"Shuyuan Wu, Xuening Zhu, Hansheng Wang","doi":"10.5705/ss.202021.0257","DOIUrl":"https://doi.org/10.5705/ss.202021.0257","url":null,"abstract":"Modern statistical analysis often encounters datasets with large sizes. For these datasets, conventional estimation methods can hardly be used immediately because practitioners often suffer from limited computational resources. In most cases, they do not have powerful computational resources (e.g., Hadoop or Spark). How to practically analyze large datasets with limited computational resources then becomes a problem of great importance. To solve this problem, we propose here a novel subsampling-based method with jackknifing. The key idea is to treat the whole sample data as if they were the population. Then, multiple subsamples with greatly reduced sizes are obtained by the method of simple random sampling with replacement. It is remarkable that we do not recommend sampling methods without replacement because this would incur a significant cost for data processing on the hard drive. Such cost does not exist if the data are processed in memory. Because subsampled data have relatively small sizes, they can be comfortably read into computer memory as a whole and then processed easily. Based on subsampled datasets, jackknife-debiased estimators can be obtained for the target parameter. The resulting estimators are statistically consistent, with an extremely small bias. Finally, the jackknife-debiased estimators from different subsamples are averaged together to form the final estimator. We theoretically show that the final estimator is consistent and asymptotically normal. Its asymptotic statistical efficiency can be as good as that of the whole sample estimator under very mild conditions. The proposed method is simple enough to be easily implemented on most practical computer systems and thus should have very wide applicability.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48682548","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

Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction 高维充分降维中的无切片逆回归

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-04-13 DOI: 10.5705/ss.202022.0112

Qing Mai, X. Shao, Runmin Wang, Xin Zhang

{"title":"Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction","authors":"Qing Mai, X. Shao, Runmin Wang, Xin Zhang","doi":"10.5705/ss.202022.0112","DOIUrl":"https://doi.org/10.5705/ss.202022.0112","url":null,"abstract":"Sliced inverse regression (SIR, Li 1991) is a pioneering work and the most recognized method in sufficient dimension reduction. While promising progress has been made in theory and methods of high-dimensional SIR, two remaining challenges are still nagging high-dimensional multivariate applications. First, choosing the number of slices in SIR is a difficult problem, and it depends on the sample size, the distribution of variables, and other practical considerations. Second, the extension of SIR from univariate response to multivariate is not trivial. Targeting at the same dimension reduction subspace as SIR, we propose a new slicing-free method that provides a unified solution to sufficient dimension reduction with high-dimensional covariates and univariate or multivariate response. We achieve this by adopting the recently developed martingale difference divergence matrix (MDDM, Lee&Shao 2018) and penalized eigen-decomposition algorithms. To establish the consistency of our method with a high-dimensional predictor and a multivariate response, we develop a new concentration inequality for sample MDDM around its population counterpart using theories for U-statistics, which may be of independent interest. Simulations and real data analysis demonstrate the favorable finite sample performance of the proposed method.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41485273","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

Distributed Logistic Regression for Massive Data with Rare Events 具有罕见事件的海量数据的分布式逻辑回归

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-04-05 DOI: 10.5705/ss.202022.0242

Xia Li, Xuening Zhu, Hansheng Wang

引用次数: 17