Statistica Sinica最新文献_第5页

Necessary and Sufficient Conditions for Multiple Objective Optimal Regression Designs 多目标最优回归设计的充分必要条件

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-03-08 DOI: 10.5705/ss.202022.0328

Lucy L. Gao, J. Ye, Shangzhi Zeng, Julie Zhou

引用次数: 0

Sparse Sliced Inverse Regression via Cholesky Matrix Penalization 基于Cholesky矩阵惩罚的稀疏切片逆回归

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0406

Linh Nghiem, Francis K.C. Hui, Samuel Mueller, A.H.Welsh

引用次数: 1

That Prasad-Rao is Robust: Estimation of Mean Squared Prediction Error of Observed Best Predictor under Potential Model Misspecification Prasad-Rao的鲁棒性:潜在模型错配下观测最佳预测器预测误差的均方估计

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0325

Xiaohui Liu, Haiqiang Ma, Jiming Jiang

引用次数: 1

Statistical Inference for Functional Time Series 函数时间序列的统计推断

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202021.0107

Jie Li, Lijian Yang

引用次数: 1

On Construction of Nonregular Two-Level Factorial Designs With Maximum Generalized Resolutions 具有最大广义分辨率的非正则二水平阶乘设计的构造

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202021.0024

Chenlu Shi, Boxin Tang

引用次数: 1

Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data 主题级单细胞表达数据的非参数贝叶斯两级聚类

3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0337

Qiuyu Wu, Xiangyu Luo

{"title":"Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data","authors":"Qiuyu Wu, Xiangyu Luo","doi":"10.5705/ss.202020.0337","DOIUrl":"https://doi.org/10.5705/ss.202020.0337","url":null,"abstract":"The advent of single-cell sequencing opens new avenues for personalized treatment. In this paper, we address a two-level clustering problem of simultaneous subject subgroup discovery (subject level) and cell type detection (cell level) for single-cell expression data from multiple subjects. However, current statistical approaches either cluster cells without considering the subject heterogeneity or group subjects without using the single-cell information. To bridge the gap between cell clustering and subject grouping, we develop a nonparametric Bayesian model, Subject and Cell clustering for Single-Cell expression data (SCSC) model, to achieve subject and cell grouping simultaneously. SCSC does not need to prespecify the subject subgroup number or the cell type number. It automatically induces subject subgroup structures and matches cell types across subjects. Moreover, it directly models the single-cell raw count data by deliberately considering the data's dropouts, library sizes, and over-dispersion. A blocked Gibbs sampler is proposed for the posterior inference. Simulation studies and the application to a multi-subject iPSC scRNA-seq dataset validate the ability of SCSC to simultaneously cluster subjects and cells.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"18 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":"135181000","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

HIGH-DIMENSIONAL FACTOR REGRESSION FOR HETEROGENEOUS SUBPOPULATIONS. 异质亚群的高维因子回归。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202020.0145

Peiyao Wang, Quefeng Li, Dinggang Shen, Yufeng Liu

{"title":"HIGH-DIMENSIONAL FACTOR REGRESSION FOR HETEROGENEOUS SUBPOPULATIONS.","authors":"Peiyao Wang, Quefeng Li, Dinggang Shen, Yufeng Liu","doi":"10.5705/ss.202020.0145","DOIUrl":"10.5705/ss.202020.0145","url":null,"abstract":"In modern scientific research, data heterogeneity is commonly observed owing to the abundance of complex data. We propose a factor regression model for data with heterogeneous subpopulations. The proposed model can be represented as a decomposition of heterogeneous and homogeneous terms. The heterogeneous term is driven by latent factors in different subpopulations. The homogeneous term captures common variation in the covariates and shares common regression coefficients across subpopulations. Our proposed model attains a good balance between a global model and a group-specific model. The global model ignores the data heterogeneity, while the group-specific model fits each subgroup separately. We prove the estimation and prediction consistency for our proposed estimators, and show that it has better convergence rates than those of the group-specific and global models. We show that the extra cost of estimating latent factors is asymptotically negligible and the minimax rate is still attainable. We further demonstrate the robustness of our proposed method by studying its prediction error under a mis-specified group-specific model. Finally, we conduct simulation studies and analyze a data set from the Alzheimer's Disease Neuroimaging Initiative and an aggregated microarray data set to further demonstrate the competitiveness and interpretability of our proposed factor regression model.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"33 1","pages":"27-53"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583735/pdf/nihms-1892524.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49684205","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

Empirical Likelihood Using External Summary Information 使用外部摘要信息的经验似然

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202023.0056

Lyu Ni, Junchao Shao, Jinyi Wang, Lei Wang

引用次数: 0

Interval estimation for operating characteristic of continuous biomarkers with controlled sensitivity or specificity. 对连续生物标记物的操作特征进行区间估计，并控制其灵敏度或特异性。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202021.0020

Yijian Huang, Isaac Parakati, Dattatraya H Patil, Martin G Sanda

{"title":"Interval estimation for operating characteristic of continuous biomarkers with controlled sensitivity or specificity.","authors":"Yijian Huang, Isaac Parakati, Dattatraya H Patil, Martin G Sanda","doi":"10.5705/ss.202021.0020","DOIUrl":"10.5705/ss.202021.0020","url":null,"abstract":"The receiver operating characteristic (ROC) curve provides a comprehensive performance assessment of a continuous biomarker over the full threshold spectrum. Nevertheless, a medical test often dictates to operate at a certain high level of sensitivity or specificity. A diagnostic accuracy metric directly targeting the clinical utility is specificity at the controlled sensitivity level, or vice versa. While the empirical point estimation is readily adopted in practice, the nonparametric interval estimation is challenged by the fact that the variance involves density functions due to estimated threshold. In addition, even with a fixed threshold, many standard confidence intervals including the Wald interval for binomial proportion could have erratic behaviors. In this article, we are motivated by the superior performance of the score interval for binomial proportion and propose a novel extension for the biomarker problem. Meanwhile, we develop exact bootstrap and establish consistency of the bootstrap variance estimator. Both single-biomarker evaluation and two-biomarker comparison are investigated. Extensive simulation studies were conducted, demonstrating competitive performance of our proposals. An illustration with aggressive prostate cancer diagnosis is provided.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"33 1","pages":"193-214"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9485519","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

High-Dimensional Behaviour of Some Two-Sample Tests Based on Ball Divergence 基于球散度的若干双样本试验的高维行为

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-01-01 DOI: 10.5705/ss.202023.0069

Bilol Banerjee, A. Ghosh

引用次数: 0