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Asymptotic Analysis of Mis-Classified Linear Mixed Models 误分类线性混合模型的渐近分析
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202023.0082
Haiqiang Ma, Jiming Jiang
{"title":"Asymptotic Analysis of Mis-Classified Linear Mixed Models","authors":"Haiqiang Ma, Jiming Jiang","doi":"10.5705/ss.202023.0082","DOIUrl":"https://doi.org/10.5705/ss.202023.0082","url":null,"abstract":"Analysis of Mis-Classified Linear Mixed Models","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":"70939976","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
Weighted Nonlinear Regression With Nonstationary Time Series 非平稳时间序列的加权非线性回归
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202021.0426
Chunlei Jin, Qiying Wang
{"title":"Weighted Nonlinear Regression With Nonstationary Time Series","authors":"Chunlei Jin, Qiying Wang","doi":"10.5705/ss.202021.0426","DOIUrl":"https://doi.org/10.5705/ss.202021.0426","url":null,"abstract":": This study investigates a weighted least squares (WLS) estimation in a nonlinear cointegrating regression. In a nonlinear regression model, where the regressors include nearly integrated arrays and stationary processes, we show that the WLS estimator has a mixed Gaussian limit, and the corresponding Studentized statistic converges to a standard normal distribution. The WLS estimator is free of the memory parameter, even when a fractional process is included in the regressors. We also consider an ordinary least squares estimation in a nonlinear cointegrating regression. Compared with the WLS estimator, the limit distribution of the ordinary least squares estimator is non-Gaussian, and depends on the nuisance parameters from the regressors when the regression function is non-integrable.","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":"70938308","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
Asymptotically Optimal Multistage Tests for Non-iid Data 非id数据的渐近最优多阶段检验
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202022.0235
Yiming Xing, Georgios Fellouris
{"title":"Asymptotically Optimal Multistage Tests for Non-iid Data","authors":"Yiming Xing, Georgios Fellouris","doi":"10.5705/ss.202022.0235","DOIUrl":"https://doi.org/10.5705/ss.202022.0235","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":"70939160","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
Test for Conditional Variance of Integer-Valued Time Series 整数值时间序列条件方差的检验
3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0357
Yuichi Goto, Kou Fujimori
{"title":"Test for Conditional Variance of Integer-Valued Time Series","authors":"Yuichi Goto, Kou Fujimori","doi":"10.5705/ss.202020.0357","DOIUrl":"https://doi.org/10.5705/ss.202020.0357","url":null,"abstract":"","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"9 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":"135182951","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
Poisson Regression With Error Corrupted High Dimensional Features 误差破坏高维特征的泊松回归
3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0251
Fei Jiang, Yanyuan Ma
{"title":"Poisson Regression With Error Corrupted High Dimensional Features","authors":"Fei Jiang, Yanyuan Ma","doi":"10.5705/ss.202020.0251","DOIUrl":"https://doi.org/10.5705/ss.202020.0251","url":null,"abstract":"","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"109 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":"135182928","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
Copula-Based Functional Bayes Classification With Principal Components and Partial Least Squares 基于copula的主成分与偏最小二乘泛函贝叶斯分类
3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0214
Wentian Huang, David Ruppert
{"title":"Copula-Based Functional Bayes Classification With Principal Components and Partial Least Squares","authors":"Wentian Huang, David Ruppert","doi":"10.5705/ss.202020.0214","DOIUrl":"https://doi.org/10.5705/ss.202020.0214","url":null,"abstract":"We present a new functional Bayes classifier that uses principal component (PC) or partial least squares (PLS) scores from the common covariance function, that is, the covariance function marginalized over groups. When the groups have different covariance functions, the PC or PLS scores need not be independent or even uncorrelated. We use copulas to model the dependence. Our method is semiparametric; the marginal densities are estimated nonparametrically by kernel smoothing and the copula is modeled parametrically. We focus on Gaussian and t-copulas, but other copulas could be used. The strong performance of our methodology is demonstrated through simulation, real data examples, and asymptotic properties.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"167 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":"135783353","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 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
Parametric Modal Regression with Autocorrelated Error Process 具有自相关误差过程的参数模态回归
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202021.0405
Tao Wang
{"title":"Parametric Modal Regression with Autocorrelated Error Process","authors":"Tao Wang","doi":"10.5705/ss.202021.0405","DOIUrl":"https://doi.org/10.5705/ss.202021.0405","url":null,"abstract":"","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"43 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937994","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
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
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
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