Statistica Sinica最新文献_第6页

PENALIZED REGRESSION FOR MULTIPLE TYPES OF MANY FEATURES WITH MISSING DATA. 对数据缺失的多种类型特征进行惩罚回归。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-04-01 DOI: 10.5705/ss.202020.0401

Kin Yau Wong, Donglin Zeng, D Y Lin

引用次数: 0

Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data. 利用区间删失竞争风险数据对一类部分线性变换模型进行筛式估计。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-04-01 DOI: 10.5705/ss.202021.0051

Xuewen Lu, Yan Wang, Dipankar Bandyopadhyay, Giorgos Bakoyannis

引用次数: 0

HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES. 整合多来源高维数据与癌症研究应用的异质性分析。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2023-04-01 DOI: 10.5705/ss.202021.0002

Tingyan Zhong, Qingzhao Zhang, Jian Huang, Mengyun Wu, Shuangge Ma

{"title":"HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.","authors":"Tingyan Zhong, Qingzhao Zhang, Jian Huang, Mengyun Wu, Shuangge Ma","doi":"10.5705/ss.202021.0002","DOIUrl":"10.5705/ss.202021.0002","url":null,"abstract":"This study has been motivated by cancer research, in which heterogeneity analysis plays an important role and can be roughly classified as unsupervised or supervised. In supervised heterogeneity analysis, the finite mixture of regression (FMR) technique is used extensively, under which the covariates affect the response differently in subgroups. High-dimensional molecular and, very recently, histopathological imaging features have been analyzed separately and shown to be effective for heterogeneity analysis. For simpler analysis, they have been shown to contain overlapping, but also independent information. In this article, our goal is to conduct the first and more effective FMR-based cancer heterogeneity analysis by integrating high-dimensional molecular and histopathological imaging features. A penalization approach is developed to regularize estimation, select relevant variables, and, equally importantly, promote the identification of independent information. Consistency properties are rigorously established. An effective computational algorithm is developed. A simulation and an analysis of The Cancer Genome Atlas (TCGA) lung cancer data demonstrate the practical effectiveness of the proposed approach. Overall, this study provides a practical and useful new way of conducting supervised cancer heterogeneity analysis.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"33 2","pages":"729-758"},"PeriodicalIF":1.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10686523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463958","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

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