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INTEGRATING INCOMPLETE DATA FOR MEDIATION ANALYSIS. 整合不完整的数据进行中介分析。
IF 1.2 3区 数学
Statistica Sinica Pub Date : 2024-04-01 DOI: 10.5705/ss.202021.0373
Andriy Derkach, Joshua N Sampson, Ruth M Pfeiffer
{"title":"INTEGRATING INCOMPLETE DATA FOR MEDIATION ANALYSIS.","authors":"Andriy Derkach, Joshua N Sampson, Ruth M Pfeiffer","doi":"10.5705/ss.202021.0373","DOIUrl":"10.5705/ss.202021.0373","url":null,"abstract":"<p><p>Mediation analysis examines the relationships between an exposure, a mediator, and an outcome. Although many approaches are available for performing such analyses they all require access to a single complete data set that contains the three key variables: outcome, exposure, and mediator. Here, we propose semiparametric methods for mediation analysis to estimate the standard causal parameters (direct and indirect effects) by combining information from several incomplete data sets, each containing only two of the three key variables. Importantly, our methods also handle scenarios in which only summary statistics based on those data sets are available. The resulting estimates of the causal parameters are asymptotically unbiased and normally distributed. We evaluate the performance of our methods in finite samples using simulations, and quantify the loss in efficiency from the lack of a complete data set with all three variables. We then apply proposed method to determine whether the number of terminal duct lobular units in the breast mediate the relationship between a polygenic risk score and breast cancer risk.</p>","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":"1045-1066"},"PeriodicalIF":1.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70938273","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
Impact Analysis for Spatial Autoregressive Models: With Application to Air Pollution in China 空间自回归模型对中国大气污染的影响分析
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0119
Hsuan-Yu Chang, Jihai Yu
{"title":"Impact Analysis for Spatial Autoregressive Models: With Application to Air Pollution in China","authors":"Hsuan-Yu Chang, Jihai Yu","doi":"10.5705/ss.202021.0119","DOIUrl":"https://doi.org/10.5705/ss.202021.0119","url":null,"abstract":": In this paper, we investigate impact analysis and its asymptotic inference for spatial autoregressive models. LeSage and Pace (2009) introduce impact analysis for spatial models and use Monte Carlo simulations to compute the dispersion. We propose to use the delta method, which enables us to obtain the dispersion in an explicit form. In addition, we provide the element-wise impact analysis. We first study the cross-sectional case, where various impacts are introduced to measure the interaction and feedback effects in a space dimension. We then study the spatial dynamic panel case with simultaneous spatial and dynamic feedback involved in the impacts. Monte Carlo results show that the proposed impact analysis has satisfactory finite sample properties. Finally, we apply impact analysis to investigate how meteorological factors and air pollutants affect PM 2 . 5 in Chinese cities.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"36 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937305","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
Nonlinear dimension reduction for functional data with application to clustering 函数数据非线性降维及其在聚类中的应用
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0393
Ruoxu Tan, Yiming Zang, G. Yin
{"title":"Nonlinear dimension reduction for functional data with application to clustering","authors":"Ruoxu Tan, Yiming Zang, G. Yin","doi":"10.5705/ss.202021.0393","DOIUrl":"https://doi.org/10.5705/ss.202021.0393","url":null,"abstract":"Nonlinear dimension reduction for functional data with application to clustering","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":"70937980","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
Unbiased Boosting Estimation for Censored Survival Data 删节生存数据的无偏增强估计
IF 1.4 3区 数学
Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0050
Li‐Pang Chen, G. Yi
{"title":"Unbiased Boosting Estimation for Censored Survival Data","authors":"Li‐Pang Chen, G. Yi","doi":"10.5705/ss.202021.0050","DOIUrl":"https://doi.org/10.5705/ss.202021.0050","url":null,"abstract":": Boosting methods have been broadly discussed for various settings, and most methods handle data with complete observations. Although some methods are available for survival data with censored responses, they tend to assume a specific model for the survival process, and most provide numerical implementation procedures without rigorous theoretical justifications. In this paper, we develop an unbiased boosting estimation method for censored survival data, without assuming an explicit model, and explore three strategies for adjusting the loss functions, while accommodating censoring effects. We implement the proposed method using a functional gradient descent algorithm, and rigorously establish our theoretical results, including the consistency and optimization convergence. Our numerical studies show that the proposed method exhibits satisfactory performance in finite-sample settings.","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":"70936904","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
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
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