发布求助
文献互助 智能选刊 最新文献

Statistica Sinica最新文献

筛选
英文 中文
A Langevinized Ensemble Kalman Filter for Large-Scale Dynamic Learning 用于大规模动态学习的Langevinized集成卡尔曼滤波器
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0172
Peiyi Zhang, Qifan Song, F. Liang
{"title":"A Langevinized Ensemble Kalman Filter for Large-Scale Dynamic Learning","authors":"Peiyi Zhang, Qifan Song, F. Liang","doi":"10.5705/ss.202022.0172","DOIUrl":"https://doi.org/10.5705/ss.202022.0172","url":null,"abstract":"","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":"70938666","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
An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces. 再现核Hilbert空间中非参数回归的在线投影估计。
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202021.0018
Tianyu Zhang, Noah Simon
{"title":"An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces.","authors":"Tianyu Zhang,&nbsp;Noah Simon","doi":"10.5705/ss.202021.0018","DOIUrl":"https://doi.org/10.5705/ss.202021.0018","url":null,"abstract":"<p><p>The goal of nonparametric regression is to recover an underlying regression function from noisy observations, under the assumption that the regression function belongs to a prespecified infinite-dimensional function space. In the online setting, in which the observations come in a stream, it is generally computationally infeasible to refit the whole model repeatedly. As yet, there are no methods that are both computationally efficient and statistically rate optimal. In this paper, we propose an estimator for online nonparametric regression. Notably, our estimator is an empirical risk minimizer in a deterministic linear space, which is quite different from existing methods that use random features and a functional stochastic gradient. Our theoretical analysis shows that this estimator obtains a rate-optimal generalization error when the regression function is known to live in a reproducing kernel Hilbert space. We also show, theoretically and empirically, that the computational cost of our estimator is much lower than that of other rate-optimal estimators proposed for this online setting.</p>","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"33 1","pages":"127-148"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162505/pdf/nihms-1807577.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9492993","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: 6
Power Enhancement for Dimension Detection of Gaussian Signals 高斯信号维数检测的功率增强
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0315
Gaspard Bernard, Thomas Verdebout
{"title":"Power Enhancement for Dimension Detection of Gaussian Signals","authors":"Gaspard Bernard, Thomas Verdebout","doi":"10.5705/ss.202022.0315","DOIUrl":"https://doi.org/10.5705/ss.202022.0315","url":null,"abstract":"In the present section, our objective is to provide Monte-Carlo simulation results to corroborate the conclusions drawn from Proposition 1 and in Section 4. In the first simulation exercise, the objective is to illustrate Proposition 1. We generated M = 10, 000 independent samples of i.i.d. observations X (b) 1 , . . . ,X (b) 10,000, for b = 0, 1 4 , 1 2 , 1. The X (b) i ’s are i.i.d. with a common (p = 8)-dimensional Gaussian distribution with mean zero and covariance matrix","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":"70939941","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
Bandwidth Selection for Large Covariance and Precision Matrices 大协方差和精度矩阵的带宽选择
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0337
Xuehu Zhu, Jian Guo, Xu Guo, Lixing Zhu, Jiasen Zheng
{"title":"Bandwidth Selection for Large Covariance and Precision Matrices","authors":"Xuehu Zhu, Jian Guo, Xu Guo, Lixing Zhu, Jiasen Zheng","doi":"10.5705/ss.202022.0337","DOIUrl":"https://doi.org/10.5705/ss.202022.0337","url":null,"abstract":"BANDWIDTH SELECTION FOR LARGE COVARIANCE AND PRECISION MATRICES Xuehu Zhu, Jian Guo, Xu Guo, Lixing Zhu∗3,4 and Jiasen Zheng 1 School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China Academy of Mathematics and Systems Science, Chinese Academy of Sciences 3 Center for Statistics and Data Science, Beijing Normal University, Zhuhai, China Department of Mathematics, Hong Kong Baptist University, Hong Kong Center for Statistical Science, Tsinghua University, Beijing, China","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":"70940014","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
Feature-weighted elastic net: using "features of features" for better prediction. 特征加权弹性网：利用 "特征的特征 "进行更好的预测。
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0226
J Kenneth Tay, Nima Aghaeepour, Trevor Hastie, Robert Tibshirani
{"title":"Feature-weighted elastic net: using \"features of features\" for better prediction.","authors":"J Kenneth Tay, Nima Aghaeepour, Trevor Hastie, Robert Tibshirani","doi":"10.5705/ss.202020.0226","DOIUrl":"10.5705/ss.202020.0226","url":null,"abstract":"<p><p>In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additional information for better prediction. The method, which we call the <i>feature-weighted elastic net</i> (\"fwelnet\"), uses these \"features of features\" to adapt the relative penalties on the feature coefficients in the elastic net penalty. In our simulations, fwelnet outperforms the lasso in terms of test mean squared error and usually gives an improvement in true positive rate or false positive rate for feature selection. We also apply this method to early prediction of preeclampsia, where fwelnet outperforms the lasso in terms of 10-fold cross-validated area under the curve (0.86 vs. 0.80). We also provide a connection between fwelnet and the group lasso and suggest how fwelnet might be used for multi-task learning.</p>","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"33 1","pages":"259-279"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10129060/pdf/nihms-1843572.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9807052","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
ON THE CONSISTENCY OF THE LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE RENEWAL CASE 长记忆噪声驱动下随机采样模型最小二乘估计的一致性:更新情况
3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0457
Héctor Araya, Natalia Bahamonde, Lisandro Fermín, Tania Roa, Soledad Torres
{"title":"ON THE CONSISTENCY OF THE LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE RENEWAL CASE","authors":"Héctor Araya, Natalia Bahamonde, Lisandro Fermín, Tania Roa, Soledad Torres","doi":"10.5705/ss.202020.0457","DOIUrl":"https://doi.org/10.5705/ss.202020.0457","url":null,"abstract":"In this study, we prove the strong consistency of the least squares estimator in a random sampled linear regression model with long-memory noise and an independent set of random times given by renewal process sampling. Additionally, we illustrate how to work with a random number of observations up to time T = 1. A simulation study is provided to illustrate the behavior of the different terms, as well as the performance of the estimator under various values of the Hurst parameter H.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"8 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":"135181027","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 Mean Dimension Reduction Through Semi-parametric Approaches 半参数方法的分布均值降维
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0157
Zhengtian Zhu, Wang-li Xu, Liping Zhu
{"title":"Distributed Mean Dimension Reduction Through Semi-parametric Approaches","authors":"Zhengtian Zhu, Wang-li Xu, Liping Zhu","doi":"10.5705/ss.202022.0157","DOIUrl":"https://doi.org/10.5705/ss.202022.0157","url":null,"abstract":"In the present article we recast the semi-parametric mean dimension reduction approaches under a least squares framework, which turns the problem of recovering the central mean subspace into a series of problems of estimating slopes in linear regressions. It also facilitates to incorporate penalties to produce sparse solutions. We further adapt the semi-parametric mean dimension reduction approaches to distributed settings when massive data are scattered at various locations and cannot be aggregated or processed through a single machine. We propose three communication-efficient distributed algorithms, the first yields a dense solution, the second produces a sparse estimation, and the third provides an orthonormal basis. The distributed algorithms reduce the computational complexities of the pooled ones substantially. In addition, the distributed algorithms attain oracle rates after a finite number of iterations. We conduct extensive numerical studies to demonstrate the finite-sample performance of the distributed estimates and to compare with the pooled algorithms.","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":"70938597","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
Efficient Learning of Nonparametric Directed Acyclic Graph With Statistical Guarantee 具有统计保证的非参数有向无环图的有效学习
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0272
Yibo Deng, Xin He, Shaogao Lv
{"title":"Efficient Learning of Nonparametric Directed Acyclic Graph With Statistical Guarantee","authors":"Yibo Deng, Xin He, Shaogao Lv","doi":"10.5705/ss.202022.0272","DOIUrl":"https://doi.org/10.5705/ss.202022.0272","url":null,"abstract":"Efficient Learning of Nonparametric Directed Acyclic Graph With Statistical Guarantee","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"2019 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938859","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
Optimal Subsampling for Multinomial Logistic Models With Big Data 大数据下多项式Logistic模型的最优子抽样
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0277
Zhiqiang Ye, Jun Yu, Mingyao Ai
{"title":"Optimal Subsampling for Multinomial Logistic Models With Big Data","authors":"Zhiqiang Ye, Jun Yu, Mingyao Ai","doi":"10.5705/ss.202022.0277","DOIUrl":"https://doi.org/10.5705/ss.202022.0277","url":null,"abstract":"This section is dedicated to presenting the explicit forms of πij(β)’s and their derivatives, which are important parts in searching the maximum likelihood estimator and in the theoretical proofs. The categorical probability πij(β) for Models (2.1)-(2.4) can be calculated directly, and the first derivative of πij(β) with respect to β can be gotten through ∂πij(β) ∂β = πij(β) ∂ log πij(β) ∂β , (S1.1)","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":"70938873","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
Time-Varying Correlation for Noncentered Nonstationary Time Series: Simultaneous Inference and Visualization 非中心非平稳时间序列的时变相关:同时推理和可视化
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0244
Ting Zhang, Yu Shao
{"title":"Time-Varying Correlation for Noncentered Nonstationary Time Series: Simultaneous Inference and Visualization","authors":"Ting Zhang, Yu Shao","doi":"10.5705/ss.202022.0244","DOIUrl":"https://doi.org/10.5705/ss.202022.0244","url":null,"abstract":"","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":"70939168","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
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信