Statistics and Computing最新文献_第9页

Improving model choice in classification: an approach based on clustering of covariance matrices 改进分类中的模型选择：基于协方差矩阵聚类的方法

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-19 DOI: 10.1007/s11222-024-10410-y

David Rodríguez-Vítores, Carlos Matrán

{"title":"Improving model choice in classification: an approach based on clustering of covariance matrices","authors":"David Rodríguez-Vítores, Carlos Matrán","doi":"10.1007/s11222-024-10410-y","DOIUrl":"https://doi.org/10.1007/s11222-024-10410-y","url":null,"abstract":"This work introduces a refinement of the Parsimonious Model for fitting a Gaussian Mixture. The improvement is based on the consideration of clusters of the involved covariance matrices according to a criterion, such as sharing Principal Directions. This and other similarity criteria that arise from the spectral decomposition of a matrix are the bases of the Parsimonious Model. We show that such groupings of covariance matrices can be achieved through simple modifications of the CEM (Classification Expectation Maximization) algorithm. Our approach leads to propose Gaussian Mixture Models for model-based clustering and discriminant analysis, in which covariance matrices are clustered according to a parsimonious criterion, creating intermediate steps between the fourteen widely known parsimonious models. The added versatility not only allows us to obtain models with fewer parameters for fitting the data, but also provides greater interpretability. We show its usefulness for model-based clustering and discriminant analysis, providing algorithms to find approximate solutions verifying suitable size, shape and orientation constraints, and applying them to both simulation and real data examples.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"99 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169060","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

Functional mixtures-of-experts 专家功能混合物

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-03-18 DOI: 10.1007/s11222-023-10379-0

Faïcel Chamroukhi, Nhat Thien Pham, Van Hà Hoang, Geoffrey J. McLachlan

{"title":"Functional mixtures-of-experts","authors":"Faïcel Chamroukhi, Nhat Thien Pham, Van Hà Hoang, Geoffrey J. McLachlan","doi":"10.1007/s11222-023-10379-0","DOIUrl":"https://doi.org/10.1007/s11222-023-10379-0","url":null,"abstract":"We consider the statistical analysis of heterogeneous data for prediction, in situations where the observations include functions, typically time series. We extend the modeling with mixtures-of-experts (ME), as a framework of choice in modeling heterogeneity in data for prediction with vectorial observations, to this functional data analysis context. We first present a new family of ME models, named functional ME (FME), in which the predictors are potentially noisy observations, from entire functions. Furthermore, the data generating process of the predictor and the real response, is governed by a hidden discrete variable representing an unknown partition. Second, by imposing sparsity on derivatives of the underlying functional parameters via Lasso-like regularizations, we provide sparse and interpretable functional representations of the FME models called iFME. We develop dedicated expectation–maximization algorithms for Lasso-like regularized maximum-likelihood parameter estimation strategies to fit the models. The proposed models and algorithms are studied in simulated scenarios and in applications to two real data sets, and the obtained results demonstrate their performance in accurately capturing complex nonlinear relationships and in clustering the heterogeneous regression data.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"114 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140152764","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

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