Statistics and Computing最新文献_第9页

Expectile and M-quantile regression for panel data 面板数据的期望值和 M 四分位回归

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-17 DOI: 10.1007/s11222-024-10396-7

Ian Meneghel Danilevicz, Valdério Anselmo Reisen, Pascal Bondon

引用次数: 0

Matrix regression heterogeneity analysis 矩阵回归异质性分析

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-16 DOI: 10.1007/s11222-024-10401-z

Fengchuan Zhang, Sanguo Zhang, Shi-Ming Li, Mingyang Ren

{"title":"Matrix regression heterogeneity analysis","authors":"Fengchuan Zhang, Sanguo Zhang, Shi-Ming Li, Mingyang Ren","doi":"10.1007/s11222-024-10401-z","DOIUrl":"https://doi.org/10.1007/s11222-024-10401-z","url":null,"abstract":"The development of modern science and technology has facilitated the collection of a large amount of matrix data in fields such as biomedicine. Matrix data modeling has been extensively studied, which advances from the naive approach of flattening the matrix into a vector. However, existing matrix modeling methods mainly focus on homogeneous data, failing to handle the data heterogeneity frequently encountered in the biomedical field, where samples from the same study belong to several underlying subgroups, and different subgroups follow different models. In this paper, we focus on regression-based heterogeneity analysis. We propose a matrix data heterogeneity analysis framework, by combining matrix bilinear sparse decomposition and penalized fusion techniques, which enables data-driven subgroup detection, including determining the number of subgroups and subgrouping membership. A rigorous theoretical analysis is conducted, including asymptotic consistency in terms of subgroup detection, the number of subgroups, and regression coefficients. Numerous numerical studies based on simulated and real data have been constructed, showcasing the superior performance of the proposed method in analyzing matrix heterogeneous data.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"57 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140152763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Doubly robust estimation of optimal treatment regimes for survival data using an instrumental variable 利用工具变量对生存数据的最佳治疗方案进行双稳健估计

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-16 DOI: 10.1007/s11222-024-10407-7

Xia Junwen, Zhan Zishu, Zhang Jingxiao

{"title":"Doubly robust estimation of optimal treatment regimes for survival data using an instrumental variable","authors":"Xia Junwen, Zhan Zishu, Zhang Jingxiao","doi":"10.1007/s11222-024-10407-7","DOIUrl":"https://doi.org/10.1007/s11222-024-10407-7","url":null,"abstract":"In survival contexts, substantial literature exists on estimating optimal treatment regimes, where treatments are assigned based on personal characteristics to maximize the survival probability. These methods assume that a set of covariates is sufficient to deconfound the treatment-outcome relationship. However, this assumption can be limited in observational studies or randomized trials in which non-adherence occurs. Therefore, we propose a novel approach to estimating optimal treatment regimes when certain confounders are unobservable and a binary instrumental variable is available. Specifically, via a binary instrumental variable, we propose a semiparametric estimator for optimal treatment regimes by maximizing a Kaplan–Meier-like estimator of the survival function. Furthermore, to increase resistance to model misspecification, we construct novel doubly robust estimators. Since the estimators of the survival function are jagged, we incorporate kernel smoothing methods to improve performance. Under appropriate regularity conditions, the asymptotic properties are rigorously established. Moreover, the finite sample performance is evaluated through simulation studies. Finally, we illustrate our method using data from the National Cancer Institute’s prostate, lung, colorectal, and ovarian cancer screening trial.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"153 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140156495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Quantile ratio regression 定量比率回归

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-14 DOI: 10.1007/s11222-024-10406-8

Alessio Farcomeni, Marco Geraci

引用次数: 0

Robust score matching for compositional data 成分数据的稳健分数匹配

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-13 DOI: 10.1007/s11222-024-10412-w

Janice L. Scealy, Kassel L. Hingee, John T. Kent, Andrew T. A. Wood

引用次数: 0

Quantile generalized measures of correlation 广义相关量

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-12 DOI: 10.1007/s11222-024-10414-8

Xinyu Zhang, Hongwei Shi, Niwen Zhou, Falong Tan, Xu Guo

引用次数: 0

Bayesian variable selection for matrix autoregressive models 矩阵自回归模型的贝叶斯变量选择

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-11 DOI: 10.1007/s11222-024-10402-y

Alessandro Celani, Paolo Pagnottoni, Galin Jones

引用次数: 0

Large-scale correlation screening under dependence for brain functional connectivity network inference 大脑功能连接网络推断依赖性下的大规模相关性筛选

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-09 DOI: 10.1007/s11222-024-10411-x

Hanâ Lbath, Alexander Petersen, Sophie Achard

{"title":"Large-scale correlation screening under dependence for brain functional connectivity network inference","authors":"Hanâ Lbath, Alexander Petersen, Sophie Achard","doi":"10.1007/s11222-024-10411-x","DOIUrl":"https://doi.org/10.1007/s11222-024-10411-x","url":null,"abstract":"Data produced by resting-state functional Magnetic Resonance Imaging are widely used to infer brain functional connectivity networks. Such networks correlate neural signals to connect brain regions, which consist in groups of dependent voxels. Previous work has focused on aggregating data across voxels within predefined regions. However, the presence of within-region correlations has noticeable impacts on inter-regional correlation detection, and thus edge identification. To alleviate them, we propose to leverage techniques from the large-scale correlation screening literature, and derive simple and practical characterizations of the mean number of correlation discoveries that flexibly incorporate intra-regional dependence structures. A connectivity network inference framework is then presented. First, inter-regional correlation distributions are estimated. Then, correlation thresholds that can be tailored to one’s application are constructed for each edge. Finally, the proposed framework is implemented on synthetic and real-world datasets. This novel approach for handling arbitrary intra-regional correlation is shown to limit false positives while improving true positive rates.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"5 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140076119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Multiple-output quantile regression neural network 多输出量位回归神经网络

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-08 DOI: 10.1007/s11222-024-10408-6

Ruiting Hao, Xiaorong Yang

{"title":"Multiple-output quantile regression neural network","authors":"Ruiting Hao, Xiaorong Yang","doi":"10.1007/s11222-024-10408-6","DOIUrl":"https://doi.org/10.1007/s11222-024-10408-6","url":null,"abstract":"Quantile regression neural network (QRNN) model has received increasing attention in various fields to provide conditional quantiles of responses. However, almost all the available literature about QRNN is devoted to handling the case with one-dimensional responses, which presents a great limitation when we focus on the quantiles of multivariate responses. To deal with this issue, we propose a novel multiple-output quantile regression neural network (MOQRNN) model in this paper to estimate the conditional quantiles of multivariate data. The MOQRNN model is constructed by the following steps. Step 1 acquires the conditional distribution of multivariate responses by a nonparametric method. Step 2 obtains the optimal transport map that pushes the spherical uniform distribution forward to the conditional distribution through the input convex neural network (ICNN). Step 3 provides the conditional quantile contours and regions by the ICNN-based optimal transport map. In both simulation studies and real data application, comparative analyses with the existing method demonstrate that the proposed MOQRNN model is more appealing to yield excellent quantile contours, which are not only smoother but also closer to their theoretical counterparts.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"281 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140076024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Total effects with constrained features 有限制特征的总效果

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-05 DOI: 10.1007/s11222-024-10398-5

{"title":"Total effects with constrained features","authors":"","doi":"10.1007/s11222-024-10398-5","DOIUrl":"https://doi.org/10.1007/s11222-024-10398-5","url":null,"abstract":"<h3>Abstract</h3> Recent studies have emphasized the connection between machine learning feature importance measures and total order sensitivity indices (total effects, henceforth). Feature correlations and the need to avoid unrestricted permutations make the estimation of these indices challenging. Additionally, there is no established theory or approach for non-Cartesian domains. We propose four alternative strategies for computing total effects that account for both dependent and constrained features. Our first approach involves a generalized winding stairs design combined with the Knothe-Rosenblatt transformation. This approach, while applicable to a wide family of input dependencies, becomes impractical when inputs are physically constrained. Our second approach is a U-statistic that combines the Jansen estimator with a weighting factor. The U-statistic framework allows the derivation of a central limit theorem for this estimator. However, this design is computationally intensive. Then, our third approach uses derangements to significantly reduce computational burden. We prove consistency and central limit theorems for these estimators as well. Our fourth approach is based on a nearest-neighbour intuition and it further reduces computational burden. We test these estimators through a series of increasingly complex computational experiments with features constrained on compact and connected domains (circle, simplex), non-compact and non-connected domains (Sierpinski gaskets), we provide comparisons with machine learning approaches and conclude with an application to a realistic simulator.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"8 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140035815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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