发布求助
文献互助 智能选刊 最新文献

Statistics and Its Interface最新文献

筛选
英文 中文
Flexible quasi-beta prime regression models for dependent continuous positive data 针对依赖性连续正数据的灵活准贝塔质回归模型
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii762
João Freitas, Juvêncio Nobre, Caio Azevedo
{"title":"Flexible quasi-beta prime regression models for dependent continuous positive data","authors":"João Freitas, Juvêncio Nobre, Caio Azevedo","doi":"10.4310/22-sii762","DOIUrl":"https://doi.org/10.4310/22-sii762","url":null,"abstract":"In many situations of interest, it is common to observe positive responses measured along several assessment conditions, within the same subjects. Usually, such a scenario implies a positive skewness on the response distributions, along with the existence of within-subject dependency. It is known that neglecting these features can lead to a misleading inference. In this paper we extend the beta prime regression model for modeling asymmetric positive data, while taking into account the dependence structure. We consider a useful predictor for modeling a suitable transformation of the mean, along with homogeneous covariance structure. The proposed model is an interesting competitor of the flexible Tweedie regression models, which include distributions such as Gamma and Inverse Gaussian. Furthermore, residual analysis and influence diagnostic tools are proposed. A Monte Carlo experiment is conducted to evaluate the performance of the proposed methodology, under small and moderate sample sizes, along with suitable discussions. The methodology is illustrated with the analysis of a real longitudinal dataset. An R package was developed to allow the practitioners to use the methodology described in this paper.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"67 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Default Bayesian testing for the zero-inflated Poisson distribution 零膨胀泊松分布的默认贝叶斯测试
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii750
Yewon Han, Haewon Hwang, Hon Keung Ng, Seong Kim
{"title":"Default Bayesian testing for the zero-inflated Poisson distribution","authors":"Yewon Han, Haewon Hwang, Hon Keung Ng, Seong Kim","doi":"10.4310/22-sii750","DOIUrl":"https://doi.org/10.4310/22-sii750","url":null,"abstract":"In a Bayesian model selection and hypothesis testing, users should be cautious when choosing suitable prior distributions, as it is an important problem. More often than not, objective Bayesian analyses utilize noninformative priors such as Jeffreys priors. However, since these noninformative priors are often improper, the Bayes factor associated with these improper priors is not well-defined. To circumvent this indeterminate issue, the Bayes factor can be corrected by intrinsic and fractional methods. These adjusted Bayes factors are asymptotically equivalent to the ordinary Bayes factors calculated with proper priors, called intrinsic priors. In this article, we derive intrinsic priors for testing the point null hypothesis under a zero-inflated Poisson distribution. Extensive simulation studies are performed to support the theoretical results on asymptotic equivalence, and two real datasets are analyzed to illustrate the methodology developed in this paper.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"5 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Estimating extreme value index by subsampling for massive datasets with heavy-tailed distributions 通过子采样估计重尾分布海量数据集的极值指数
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-07-19 DOI: 10.4310/22-sii749
Yongxin Li, Liujun Chen, Deyuan Li, Hansheng Wang
{"title":"Estimating extreme value index by subsampling for massive datasets with heavy-tailed distributions","authors":"Yongxin Li, Liujun Chen, Deyuan Li, Hansheng Wang","doi":"10.4310/22-sii749","DOIUrl":"https://doi.org/10.4310/22-sii749","url":null,"abstract":"Modern statistical analyses often encounter datasets with massive sizes and heavy-tailed distributions. For datasets with massive sizes, traditional estimation methods can hardly be used to estimate the extreme value index directly. To address the issue, we propose here a subsampling-based method. Specifically, multiple subsamples are drawn from the whole dataset by using the technique of simple random subsampling with replacement. Based on each subsample, an approximate maximum likelihood estimator can be computed. The resulting estimators are then averaged to form a more accurate one. Under appropriate regularity conditions, we show theoretically that the proposed estimator is consistent and asymptotically normal. With the help of the estimated extreme value index, we can estimate high-level quantiles and tail probabilities of a heavy-tailed random variable consistently. Extensive simulation experiments are provided to demonstrate the promising performance of our method. A real data analysis is also presented for illustration purpose.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"27 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141743408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
A random projection method for large-scale community detection 大规模群落探测的随机投影法
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-02-01 DOI: 10.4310/22-sii752
Haobo Qi, Hansheng Wang, Xuening Zhu
{"title":"A random projection method for large-scale community detection","authors":"Haobo Qi, Hansheng Wang, Xuening Zhu","doi":"10.4310/22-sii752","DOIUrl":"https://doi.org/10.4310/22-sii752","url":null,"abstract":"In this work, we consider a random projection method for a large-scale community detection task. We introduce a random Gaussian matrix that generates several projections on the column space of the network adjacency matrix. The $k$-means algorithm is then applied with the low-dimensional projected matrix. The computational complexity is much lower than that of the classic spectral clustering methods. Furthermore, the algorithm is easy to implement and accessible for privacy preservation. We can theoretically establish a strong consistency result of the algorithm under the stochastic block model. Extensive numerical studies are conducted to verify the theoretical findings and illustrate the usefulness of the proposed method.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Correlated Wishart matrices classification via an expectation-maximization composite likelihood-based algorithm 通过基于期望最大化的复合似然算法进行相关 Wishart 矩阵分类
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-02-01 DOI: 10.4310/22-sii770
Zhou Lan
{"title":"Correlated Wishart matrices classification via an expectation-maximization composite likelihood-based algorithm","authors":"Zhou Lan","doi":"10.4310/22-sii770","DOIUrl":"https://doi.org/10.4310/22-sii770","url":null,"abstract":"Positive-definite matrix-variate data is becoming popular in computer vision. The computer vision data descriptors in the form of Region Covariance Descriptors (RCD) are positive definite matrices, which extract the key features of the images. The RCDs are extensively used in image set classification. Some classification methods treating RCDs as Wishart distributed random matrices are being proposed. However, the majority of the current methods preclude the potential correlation among the RCDs caused by the so-called auxiliary information (e.g., subjects’ ages and nose widths, etc). Modeling correlated Wishart matrices is difficult since the joint density function of correlated Wishart matrices is difficult to be obtained. In this paper, we propose an Expectation-Maximization composite likelihoodbased algorithm of Wishart matrices to tackle this issue. Given the numerical studies based on the synthetic data and the real data (Chicago face data-set), our proposed algorithm performs better than the alternative methods which do not consider the correlation caused by the so-called auxiliary information.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"27 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Robust and covariance-assisted tensor response regression 稳健和协方差辅助张量响应回归
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-02-01 DOI: 10.4310/sii.2024.v17.n2.a10
Ning Wang, Xin Zhang
{"title":"Robust and covariance-assisted tensor response regression","authors":"Ning Wang, Xin Zhang","doi":"10.4310/sii.2024.v17.n2.a10","DOIUrl":"https://doi.org/10.4310/sii.2024.v17.n2.a10","url":null,"abstract":"Tensor data analysis is gaining increasing popularity in modern multivariate statistics. When analyzing real-world tensor data, many existing tensor estimation approaches are sensitive to heavy-tailed data and outliers, in addition to the apparent high-dimensionality. In this article, we develop a robust and covariance-assisted tensor response regression model based on a recently proposed tensor t‑distribution to address these issues in tensor data. This model assumes that the tensor regression coefficient has a low-rank structure that can be learned more effectively using the additional covariance information. This enables a fast and robust decomposition-based estimation method. Theoretical analysis and numerical experiments demonstrate the superior performance of our approach. By addressing the heavy-tail, high-order, and high-dimensional issues, our work contributes to robust and effective estimation methods for tensor response regression, with broad applicability in various domains.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"24 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Bayesian tensor-on-tensor regression with efficient computation 高效计算的贝叶斯张量对张量回归
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-02-01 DOI: 10.4310/23-sii786
Kunbo Wang, Yanxun Xu
{"title":"Bayesian tensor-on-tensor regression with efficient computation","authors":"Kunbo Wang, Yanxun Xu","doi":"10.4310/23-sii786","DOIUrl":"https://doi.org/10.4310/23-sii786","url":null,"abstract":"We propose a Bayesian tensor-on-tensor regression approach to predict a multidimensional array (tensor) of arbitrary dimensions from another tensor of arbitrary dimensions, building upon the Tucker decomposition of the regression coefficient tensor. Traditional tensor regression methods making use of the Tucker decomposition either assume the dimension of the core tensor to be known or estimate it via cross-validation or some model selection criteria. However, no existing method can simultaneously estimate the model dimension (the dimension of the core tensor) and other model parameters. To fill this gap, we develop an efficient Markov Chain Monte Carlo (MCMC) algorithm to estimate both the model dimension and parameters for posterior inference. Besides the MCMC sampler, we also develop an ultra-fast optimization-based computing algorithm wherein the maximum <i>a posteriori</i> estimators for parameters are computed, and the model dimension is optimized via a simulated annealing algorithm. The proposed Bayesian framework provides a natural way for uncertainty quantification. Through extensive simulation studies, we evaluate the proposed Bayesian tensor-on-tensor regression model and show its superior performance compared to alternative methods. We also demonstrate its practical effectiveness by applying it to two real-world datasets, including facial imaging data and 3D motion data.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"23 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139658968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Density-convoluted tensor support vector machines 密度卷积张量支持向量机
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-02-01 DOI: 10.4310/23-sii796
Boxiang Wang, Le Zhou, Jian Yang, Qing Mai
{"title":"Density-convoluted tensor support vector machines","authors":"Boxiang Wang, Le Zhou, Jian Yang, Qing Mai","doi":"10.4310/23-sii796","DOIUrl":"https://doi.org/10.4310/23-sii796","url":null,"abstract":"With the emergence of tensor data (also known as multi-dimensional arrays) in many modern applications such as image processing and digital marketing, tensor classification is gaining increasing attention. Although there is a rich toolbox of classification methods for vector-based data, these traditional methods may not be adequate for tensor data classification. In this paper, we propose a new classifier called density-convoluted tensor support vector machine (DCT‑SVM). This method is motivated by applying a kernel density convolution method on the SVM loss to induce a new family of classification loss functions. To establish the theoretical foundation of DCT‑SVM, the probabilistic order of magnitude for its excess risk is systematically studied. For efficiently computing DCT‑SVM, we develop a fast monotone accelerated proximal gradient descent algorithm and show the convergence of the algorithm. With simulation studies, we demonstrate the superior performance of DCT‑SVM over many popular classification methods. We further demonstrate the real potential of DCT‑SVM using a modern data application for online advertising.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"36 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Multi-way overlapping clustering by Bayesian tensor decomposition 通过贝叶斯张量分解进行多向重叠聚类
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-02-01 DOI: 10.4310/23-sii790
Zhuofan Wang, Fangting Zhou, Kejun He, Yang Ni
{"title":"Multi-way overlapping clustering by Bayesian tensor decomposition","authors":"Zhuofan Wang, Fangting Zhou, Kejun He, Yang Ni","doi":"10.4310/23-sii790","DOIUrl":"https://doi.org/10.4310/23-sii790","url":null,"abstract":"The development of modern sequencing technologies provides great opportunities to measure gene expression of multiple tissues from different individuals. The three-way variation across genes, tissues, and individuals makes statistical inference a challenging task. In this paper, we propose a Bayesian multi-way clustering approach to cluster genes, tissues, and individuals simultaneously. The proposed model adaptively trichotomizes the observed data into three latent categories and uses a Bayesian hierarchical construction to further decompose the latent variables into lower-dimensional features, which can be interpreted as overlapping clusters. With a Bayesian nonparametric prior, i.e., the Indian buffet process, our method determines the cluster number automatically. The utility of our approach is demonstrated through simulation studies and an application to the Genotype-Tissue Expression (GTEx) RNA-seq data. The clustering result reveals some interesting findings about depression-related genes in human brain, which are also consistent with biological domain knowledge. The detailed algorithm and some numerical results are available in the online Supplementary Material, available at $href{https://intlpress.com/site/pub/files/supp/sii/2024/0017/0002/sii-2024-0017-0002-s001.pdf}{ https://intlpress.com/site/pub/files/supp/sii/2024/0017/0002/sii-2024-0017-0002-s001.pdf}.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"12 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Community detection in temporal citation network via a tensor-based approach 基于张量的时态引文网络社群检测方法
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2024-02-01 DOI: 10.4310/22-sii751
Tianchen Gao, Rui Pan, Junfei Zhang, Hansheng Wang
{"title":"Community detection in temporal citation network via a tensor-based approach","authors":"Tianchen Gao, Rui Pan, Junfei Zhang, Hansheng Wang","doi":"10.4310/22-sii751","DOIUrl":"https://doi.org/10.4310/22-sii751","url":null,"abstract":"In the era of big data, network analysis has attracted widespread attention. Detecting and tracking community evolution in temporal networks can uncover important and interesting behaviors. In this paper, we analyze a temporal citation network constructed by publications collected from 44 statistical journals between 2001 and 2018. We propose an approach named Tensor-based Directed Spectral Clustering On Ratios of Eigenvectors (TD-SCORE) which can correct for degree heterogeneity to detect the community structure of the temporal citation network. We first explore the characteristics of the temporal network via in-degree distribution and visualization of different snapshots, and we find that both the community structure and the key nodes change over time. Then, we apply the TD-SCORE method to the core network of our temporal citation network. Seven communities are identified, including variable selection, Bayesian analysis, functional data analysis, and many others. Finally, we track the evolution of the above communities and reach some conclusions.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"26 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139659443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书