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Parsimonious Tensor Discriminant Analysis 简约张量判别分析
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202020.0496
Ning Wang, Wenjing Wang, Xin Zhang
{"title":"Parsimonious Tensor Discriminant Analysis","authors":"Ning Wang, Wenjing Wang, Xin Zhang","doi":"10.5705/ss.202020.0496","DOIUrl":"https://doi.org/10.5705/ss.202020.0496","url":null,"abstract":": Discriminant analyses of multidimensional array data (i.e., tensors) are of substantial interest in numerous statistics and engineering research problems, such as signal processing, imaging, genetics, and brain–computer interfaces. In this study, we consider a multi-class discriminant analysis with a tensor-variate predictor and a categorical response. To overcome the high dimensionality and to exploit the tensor correlation structure, we propose the discriminant analysis with tensor envelope (DATE) model for simultaneous dimension reduction and classification. We extend the notion of tensor envelopes from regression to discriminant analysis and develop two complementary estimation procedures: DATE-L is a likelihood-based estimator that is shown to be asymptotically efficient when the sample size goes to infinity and the tensor dimension is fixed; DATE-D is a novel decomposition-based estimator suitable for high-dimensional problems. Interestingly, we show that DATE-D is still root-n consistent, even when the tensor dimensions on each model grow arbitrarily fast, but at a similar rate. We demonstrate the robustness and effi-ciency of our estimators using extensive simulations and real-data examples.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70936940","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
A Zero-imputation Approach in Recommendation Systems with Data Missing Heterogeneously 数据异构缺失推荐系统中的零归一化方法
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0429
Jiashen Lu, Kehui Chen
{"title":"A Zero-imputation Approach in Recommendation Systems with Data Missing Heterogeneously","authors":"Jiashen Lu, Kehui Chen","doi":"10.5705/ss.202021.0429","DOIUrl":"https://doi.org/10.5705/ss.202021.0429","url":null,"abstract":"","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938173","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
Kernel Regression Utilizing External Information as Constraints 利用外部信息作为约束的核回归
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0446
Chi-Shian Dai, Jun Shao
{"title":"Kernel Regression Utilizing External Information as Constraints","authors":"Chi-Shian Dai, Jun Shao","doi":"10.5705/ss.202021.0446","DOIUrl":"https://doi.org/10.5705/ss.202021.0446","url":null,"abstract":"","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938185","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
Sharp Bounds for Variance of Treatment Effect Estimators in the Presence of Covariates 协变量存在下处理效应估计量方差的锐界
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0351
Ruoyu P. T. Wang, Qihua Wang, Wang Miao, Xiaohua Zhou
{"title":"Sharp Bounds for Variance of Treatment Effect Estimators in the Presence of Covariates","authors":"Ruoyu P. T. Wang, Qihua Wang, Wang Miao, Xiaohua Zhou","doi":"10.5705/ss.202021.0351","DOIUrl":"https://doi.org/10.5705/ss.202021.0351","url":null,"abstract":"The supplementary","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937449","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
Measures of Uncertainty for Shrinkage Model Selection 收缩模型选择的不确定性度量
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0281
Yuanyuan Li, Jiming Jiang
{"title":"Measures of Uncertainty for Shrinkage Model Selection","authors":"Yuanyuan Li, Jiming Jiang","doi":"10.5705/ss.202021.0281","DOIUrl":"https://doi.org/10.5705/ss.202021.0281","url":null,"abstract":"","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937682","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
A Systematic View of Information-Based Optimal Subdata Selection: Algorithm Development, Performance Evaluation, and Application in Financial Data 基于信息的最优子数据选择的系统观点:算法开发、性能评估及其在金融数据中的应用
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202022.0019
Li He, W. Li, Difan Song, Min-Seok Yang
{"title":"A Systematic View of Information-Based Optimal Subdata Selection: Algorithm Development, Performance Evaluation, and Application in Financial Data","authors":"Li He, W. Li, Difan Song, Min-Seok Yang","doi":"10.5705/ss.202022.0019","DOIUrl":"https://doi.org/10.5705/ss.202022.0019","url":null,"abstract":"A Systematic View of Information-Based Optimal","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"12 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937942","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
Functional Threshold Autoregressive Model 功能阈值自回归模型
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202022.0096
Yuanbo Li, Kun Chen, Xunze Zheng, C. Yau
{"title":"Functional Threshold Autoregressive Model","authors":"Yuanbo Li, Kun Chen, Xunze Zheng, C. Yau","doi":"10.5705/ss.202022.0096","DOIUrl":"https://doi.org/10.5705/ss.202022.0096","url":null,"abstract":": We propose a functional threshold autoregressive model for flexible functional time series modeling. In particular, the behavior of a function at a given time point can be described by different autoregressive mechanisms, depending on the values of a threshold variable at a past time point. Sufficient conditions for the strict stationarity and ergodicity of the functional threshold autoregressive process are investigated. We develop a novel criterion-based method simultaneously conducting dimension reduction and estimating the thresholds, autoregressive orders, and model parameters. We also establish the consistency and asymptotic distributions of the estimators of both thresholds and the underlying autoregressive models. Simulation studies and an application to U.S. Treasury zero-coupon yield rates are provided to illustrate the effectiveness and usefulness of the proposed methodology.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938567","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
Grouped Network Poisson Autoregressive Model 分组网络泊松自回归模型
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202022.0040
Yuxin Tao, Dongyu Li, Xiaoyue Niu
{"title":"Grouped Network Poisson Autoregressive Model","authors":"Yuxin Tao, Dongyu Li, Xiaoyue Niu","doi":"10.5705/ss.202022.0040","DOIUrl":"https://doi.org/10.5705/ss.202022.0040","url":null,"abstract":"Grouped Network Poisson Autoregressive Model","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938717","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
Dynamic Copula-Based Nonparametric Estimation of Rank-Tracking Probabilities With Longitudinal Data 纵向数据下基于动态copula的秩跟踪概率非参数估计
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0422
Xiaoyu Zhang, Mixia Wu, Colin O. Wu
{"title":"Dynamic Copula-Based Nonparametric Estimation of Rank-Tracking Probabilities With Longitudinal Data","authors":"Xiaoyu Zhang, Mixia Wu, Colin O. Wu","doi":"10.5705/ss.202021.0422","DOIUrl":"https://doi.org/10.5705/ss.202021.0422","url":null,"abstract":": The rank-tracking probability (RTP) is a useful statistical index for measuring the “tracking ability” of longitudinal disease risk factors in biomedical studies. A flexible nonparametric method for estimating the RTP is the two-step un-structured kernel smoothing estimator, which can be applied when there are time-invariant and categorical covariates. We propose a dynamic copula-based smoothing method for estimating the RTP, and show that it is both theoretically and practically superior to the unstructured smoothing method. We derive the asymptotic mean squared errors of the copula-based kernel smoothing estimators, and use a simulation study to show that the proposed method has smaller empirical mean squared errors than those of the unstructured smoothing method. We apply the proposed estimation method to a longitudinal epidemiological study and show that it leads to clinically meaningful findings in biomedical applications.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938132","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
A Projection-Based Diagnostic Test for Generalized Functional Regression Models 基于投影的广义功能回归模型诊断测试
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202022.0083
Guizhen Li, Mengying You, Lingzhi Zhou, Hua Liang, Huazhen Lin
{"title":"A Projection-Based Diagnostic Test for Generalized Functional Regression Models","authors":"Guizhen Li, Mengying You, Lingzhi Zhou, Hua Liang, Huazhen Lin","doi":"10.5705/ss.202022.0083","DOIUrl":"https://doi.org/10.5705/ss.202022.0083","url":null,"abstract":"A Projection-Based Diagnostic Test for Generalized Functional Regression Models","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938477","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
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