Statistics and Computing最新文献_第4页

A limit formula and a series expansion for the bivariate Normal tail probability 双变量正态尾概率的极限公式和数列展开

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-07-08 DOI: 10.1007/s11222-024-10466-w

Siu-Kui Au

引用次数: 0

Classifier-dependent feature selection via greedy methods 通过贪婪方法进行分类器特征选择

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-07-06 DOI: 10.1007/s11222-024-10460-2

Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti, Michele Piana, Emma Perracchione

{"title":"Classifier-dependent feature selection via greedy methods","authors":"Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti, Michele Piana, Emma Perracchione","doi":"10.1007/s11222-024-10460-2","DOIUrl":"https://doi.org/10.1007/s11222-024-10460-2","url":null,"abstract":"The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. In statistical learning, feature selection is usually realized by means of methods that are independent of the classifier applied to perform the prediction using that reduced number of features. Instead, the greedy feature selection identifies the most important feature at each step and according to the selected classifier. The benefits of such scheme are investigated in terms of model capacity indicators, such as the Vapnik-Chervonenkis dimension or the kernel alignment. This theoretical study proves that the iterative greedy algorithm is able to construct classifiers whose complexity capacity grows at each step. The proposed method is then tested numerically on various datasets and compared to the state-of-the-art techniques. The results show that our iterative scheme is able to truly capture only a few relevant features, and may improve, especially for real and noisy data, the accuracy scores of other techniques. The greedy scheme is also applied to the challenging application of predicting geo-effective manifestations of the active Sun.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575838","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

Locally sparse and robust partial least squares in scalar-on-function regression 标量函数回归中的局部稀疏稳健偏最小二乘法

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-07-06 DOI: 10.1007/s11222-024-10464-y

Sude Gurer, Han Lin Shang, Abhijit Mandal, Ufuk Beyaztas

{"title":"Locally sparse and robust partial least squares in scalar-on-function regression","authors":"Sude Gurer, Han Lin Shang, Abhijit Mandal, Ufuk Beyaztas","doi":"10.1007/s11222-024-10464-y","DOIUrl":"https://doi.org/10.1007/s11222-024-10464-y","url":null,"abstract":"We present a novel approach for estimating a scalar-on-function regression model, leveraging a functional partial least squares methodology. Our proposed method involves computing the functional partial least squares components through sparse partial robust M regression, facilitating robust and locally sparse estimations of the regression coefficient function. This strategy delivers a robust decomposition for the functional predictor and regression coefficient functions. After the decomposition, model parameters are estimated using a weighted loss function, incorporating robustness through iterative reweighting of the partial least squares components. The robust decomposition feature of our proposed method enables the robust estimation of model parameters in the scalar-on-function regression model, ensuring reliable predictions in the presence of outliers and leverage points. Moreover, it accurately identifies zero and nonzero sub-regions where the slope function is estimated, even in the presence of outliers and leverage points. We assess our proposed method’s estimation and predictive performance through a series of Monte Carlo experiments and an empirical dataset—that is, data collected in relation to oriented strand board. Compared to existing methods our proposed method performs favorably. Notably, our robust procedure exhibits superior performance in the presence of outliers while maintaining competitiveness in their absence. Our method has been implemented in the robsfplsr package in .","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575839","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

Efficient Shapley performance attribution for least-squares regression 最小二乘回归的高效夏普利性能归因

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-07-04 DOI: 10.1007/s11222-024-10459-9

Logan Bell, Nikhil Devanathan, Stephen Boyd

引用次数: 0

On weak convergence of quantile-based empirical likelihood process for ROC curves 论 ROC 曲线基于量化的经验似然过程的弱收敛性

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-07-04 DOI: 10.1007/s11222-024-10457-x

Hu Jiang, Liu Yiming, Zhou Wang

引用次数: 0

Correction to: analysis of stochastic gradient descent in continuous time 更正：连续时间内的随机梯度下降分析

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-07-02 DOI: 10.1007/s11222-024-10450-4

Jonas Latz

引用次数: 0

Systemic infinitesimal over-dispersion on graphical dynamic models 图形动态模型上的系统无穷小过度分散

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-07-02 DOI: 10.1007/s11222-024-10443-3

Ning Ning, Edward Ionides

引用次数: 0

Greedy recursive spectral bisection for modularity-bound hierarchical divisive community detection 用于模块化约束分层分裂群落检测的贪婪递归光谱分段法

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-27 DOI: 10.1007/s11222-024-10451-3

Douglas O. Cardoso, João Domingos Gomes da Silva Junior, Carla Silva Oliveira, Celso Marques, Laura Silva de Assis

{"title":"Greedy recursive spectral bisection for modularity-bound hierarchical divisive community detection","authors":"Douglas O. Cardoso, João Domingos Gomes da Silva Junior, Carla Silva Oliveira, Celso Marques, Laura Silva de Assis","doi":"10.1007/s11222-024-10451-3","DOIUrl":"https://doi.org/10.1007/s11222-024-10451-3","url":null,"abstract":"Spectral clustering techniques depend on the eigenstructure of a similarity matrix to assign data points to clusters, so that points within the same cluster exhibit high similarity and are compared to those in different clusters. This work aimed to develop a spectral method that could be compared to clustering algorithms that represent the current state of the art. This investigation conceived a novel spectral clustering method, as well as five policies that guide its execution, based on spectral graph theory and embodying hierarchical clustering principles. Computational experiments comparing the proposed method with six state-of-the-art algorithms were undertaken in this study to evaluate the clustering methods under scrutiny. The assessment was performed using two evaluation metrics, specifically the adjusted Rand index, and modularity. The obtained results furnish compelling evidence, indicating that the proposed method is competitive and possesses distinctive properties compared to those elucidated in the existing literature. This suggests that our approach stands as a viable alternative, offering a robust choice within the spectrum of available same-purpose tools.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517773","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

Flexible Bayesian quantile regression based on the generalized asymmetric Huberised-type distribution 基于广义非对称胡贝利兹型分布的灵活贝叶斯量化回归

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-27 DOI: 10.1007/s11222-024-10453-1

Weitao Hu, Weiping Zhang

引用次数: 0

Structured prior distributions for the covariance matrix in latent factor models 潜因模型中协方差矩阵的结构化先验分布

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-26 DOI: 10.1007/s11222-024-10454-0

Sarah Elizabeth Heaps, Ian Hyla Jermyn

{"title":"Structured prior distributions for the covariance matrix in latent factor models","authors":"Sarah Elizabeth Heaps, Ian Hyla Jermyn","doi":"10.1007/s11222-024-10454-0","DOIUrl":"https://doi.org/10.1007/s11222-024-10454-0","url":null,"abstract":"Factor models are widely used for dimension reduction in the analysis of multivariate data. This is achieved through decomposition of a (p times p) covariance matrix into the sum of two components. Through a latent factor representation, they can be interpreted as a diagonal matrix of idiosyncratic variances and a shared variation matrix, that is, the product of a (p times k) factor loadings matrix and its transpose. If (k ll p), this defines a parsimonious factorisation of the covariance matrix. Historically, little attention has been paid to incorporating prior information in Bayesian analyses using factor models where, at best, the prior for the factor loadings is order invariant. In this work, a class of structured priors is developed that can encode ideas of dependence structure about the shared variation matrix. The construction allows data-informed shrinkage towards sensible parametric structures while also facilitating inference over the number of factors. Using an unconstrained reparameterisation of stationary vector autoregressions, the methodology is extended to stationary dynamic factor models. For computational inference, parameter-expanded Markov chain Monte Carlo samplers are proposed, including an efficient adaptive Gibbs sampler. Two substantive applications showcase the scope of the methodology and its inferential benefits.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506393","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