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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
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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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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
Test for Zero Mean of Errors In An ARMA-GGARCH Model After Using A Median Inference 使用中值推理的ARMA-GGARCH模型的零误差均值检验
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202022.0013
Yaolan Ma, Mo Zhou, Liang Peng, Rongmao Zhang
{"title":"Test for Zero Mean of Errors In An ARMA-GGARCH Model After Using A Median Inference","authors":"Yaolan Ma, Mo Zhou, Liang Peng, Rongmao Zhang","doi":"10.5705/ss.202022.0013","DOIUrl":"https://doi.org/10.5705/ss.202022.0013","url":null,"abstract":"Test for Zero Mean of Errors In An ARMA-GGARCH Model After Using A Median Inference Abstract: The stylized fact of heavy tails makes median inferences appealing in fitting an ARMA model with heteroscedastic errors to financial returns. To ensure that the model still concerns the conditional mean, we test for a zero mean of the errors using a random weighted bootstrap method for quantifying estimation uncertainty. The proposed test is robust against heteroscedasticity and heavy tails as we do not infer the heteroscedasticity and need fewer finite moments. Simulations confirm the good finite sample performance in terms of size and power. Empirical applications caution the model interpretation after using a median inference.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937873","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
Robust Estimation of Covariance Matrices: Adversarial Contamination and Beyond 协方差矩阵的稳健估计:对抗污染及其他
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0388
Stanislav Minsker, Lang Wang
{"title":"Robust Estimation of Covariance Matrices: Adversarial Contamination and Beyond","authors":"Stanislav Minsker, Lang Wang","doi":"10.5705/ss.202021.0388","DOIUrl":"https://doi.org/10.5705/ss.202021.0388","url":null,"abstract":"","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937901","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
Scalable Estimation for High Velocity Survival Data Able to Accommodate Addition of Covariates 能够容纳协变量的高速生存数据的可扩展估计
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202022.0028
Ying Sheng, Yifei Sun, C. E. Mcculloch, Chiung-Yu Huang
{"title":"Scalable Estimation for High Velocity Survival Data Able to Accommodate Addition of Covariates","authors":"Ying Sheng, Yifei Sun, C. E. Mcculloch, Chiung-Yu Huang","doi":"10.5705/ss.202022.0028","DOIUrl":"https://doi.org/10.5705/ss.202022.0028","url":null,"abstract":"Scalable Estimation for High Velocity Survival Data Able to Accommodate Addition of Covariates Abstract: With the rapidly increasing availability of large-scale streaming data, there has been a growing interest in developing methods that allow the processing of the data in batches without requiring storage of the full dataset. In this paper, we propose a hybrid likelihood approach for scalable estimation of the Cox model using individual-level data in the current data batch and summary statistics calculated from historical data. We show that the proposed scalable estimator is asymptotically as efficient as the maximum likelihood estimator calculated using the entire dataset with low data storage requirements and low loading and computation time. A challenge in analyzing survival data batches that is not accommodated in ex-tant methods is that new covariates may become available midway through data collection. To accommodate addition of covariates, we develop a hybrid empirical likelihood approach to incorporate the historical covariate effects evaluated in a reduced Cox model. The extended scalable estimator is asymptotically more efficient than the maximum likelihood estimator obtained using only the data batches that include the additional covariates. The proposed approaches are evaluated by numerical simulations and illustrated with an analysis of Surveillance, Epidemiology, and End Results breast data.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938004","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
On Estimation of the Logarithm of the Mean Squared Prediction Error of A Mixed-effect Predictor 混合效应预测器均方预测误差的对数估计
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202022.0043
Jianling Wang, Thuan Nguyen, Y. Luan, Jiming Jiang
{"title":"On Estimation of the Logarithm of the Mean Squared Prediction Error of A Mixed-effect Predictor","authors":"Jianling Wang, Thuan Nguyen, Y. Luan, Jiming Jiang","doi":"10.5705/ss.202022.0043","DOIUrl":"https://doi.org/10.5705/ss.202022.0043","url":null,"abstract":": The mean squared prediction error (MSPE) is an important measure of uncertainty in small-area estimation. It is desirable to produce a second-order unbiased MSPE estimator, that is, the bias of the estimator is o ( m − 1 ), where m is the total number of small areas for which data are available. However, this is difficult, especially if the estimator needs to be positive, or at least nonnegative. In fact, very few MSPE estimators are both second-order unbiased and guaranteed to be positive. We consider an alternative, easier approach of estimating the logarithm of the MSPE (log-MSPE), thus avoiding the positivity problem. We derive a second-order unbiased estimator of the log-MSPE using the Prasad–Rao linearization method. The results of empirical studies demonstrate the superiority of the proposed log-MSPE estimator over a naive log-MSPE estimator and an existing method, known as McJack. Lastly, we demonstrate the proposed method by applying it to real data.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938735","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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