Computational Statistics & Data Analysis最新文献_第10页

Beta-CoRM: A Bayesian approach for n-gram profiles analysis Beta-CoRM：用于 n-gram 剖面分析的贝叶斯方法

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-09-10 DOI: 10.1016/j.csda.2024.108056

José A. Perusquía , Jim E. Griffin , Cristiano Villa

引用次数: 0

Minimum profile Hellinger distance estimation of general covariate models 一般协变量模型的最小剖面海灵格距离估计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-30 DOI: 10.1016/j.csda.2024.108054

Bowei Ding , Rohana J. Karunamuni , Jingjing Wu

{"title":"Minimum profile Hellinger distance estimation of general covariate models","authors":"Bowei Ding , Rohana J. Karunamuni , Jingjing Wu","doi":"10.1016/j.csda.2024.108054","DOIUrl":"10.1016/j.csda.2024.108054","url":null,"abstract":"<div><p>Covariate models, such as polynomial regression models, generalized linear models, and heteroscedastic models, are widely used in statistical applications. The importance of such models in statistical analysis is abundantly clear by the ever-increasing rate at which articles on covariate models are appearing in the statistical literature. Because of their flexibility, covariate models are increasingly being exploited as a convenient way to model data that consist of both a response variable and one or more covariate variables that affect the outcome of the response variable. Efficient and robust estimates for broadly defined semiparametric covariate models are investigated, and for this purpose the minimum distance approach is employed. In general, minimum distance estimators are automatically robust with respect to the stability of the quantity being estimated. In particular, minimum Hellinger distance estimation for parametric models produces estimators that are asymptotically efficient at the model density and simultaneously possess excellent robustness properties. For semiparametric covariate models, the minimum Hellinger distance method is extended and a minimum profile Hellinger distance estimator is proposed. Its asymptotic properties such as consistency are studied, and its finite-sample performance and robustness are examined by using Monte Carlo simulations and three real data analyses. Additionally, a computing algorithm is developed to ease the computation of the estimator.</p></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"202 ","pages":"Article 108054"},"PeriodicalIF":1.5,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167947324001385/pdfft?md5=cefa2d178122667194291a858ff4b934&pid=1-s2.0-S0167947324001385-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122374","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

Robust direction estimation in single-index models via cumulative divergence 通过累积发散在单指数模型中进行稳健的方向估计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-30 DOI: 10.1016/j.csda.2024.108052

Shuaida He , Jiarui Zhang , Xin Chen

引用次数: 0

A Bayesian cluster validity index 贝叶斯聚类有效性指数

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-30 DOI: 10.1016/j.csda.2024.108053

Onthada Preedasawakul , Nathakhun Wiroonsri

{"title":"A Bayesian cluster validity index","authors":"Onthada Preedasawakul , Nathakhun Wiroonsri","doi":"10.1016/j.csda.2024.108053","DOIUrl":"10.1016/j.csda.2024.108053","url":null,"abstract":"<div><p>Selecting the appropriate number of clusters is a critical step in applying clustering algorithms. To assist in this process, various cluster validity indices (CVIs) have been developed. These indices are designed to identify the optimal number of clusters within a dataset. However, users may not always seek the absolute optimal number of clusters but rather a secondary option that better aligns with their specific applications. This realization has led us to introduce a Bayesian cluster validity index (BCVI), which builds upon existing indices. The BCVI utilizes either Dirichlet or generalized Dirichlet priors, resulting in the same posterior distribution. The proposed BCVI is evaluated using the Calinski-Harabasz, CVNN, Davies–Bouldin, silhouette, Starczewski, and Wiroonsri indices for hard clustering and the KWON2, Wiroonsri–Preedasawakul, and Xie–Beni indices for soft clustering as underlying indices. The performance of the proposed BCVI with that of the original underlying indices has been compared. The BCVI offers clear advantages in situations where user expertise is valuable, allowing users to specify their desired range for the final number of clusters. To illustrate this, experiments classified into three different scenarios are conducted. Additionally, the practical applicability of the proposed approach through real-world datasets, such as MRI brain tumor images are presented. These tools are published as a recent R package ‘BayesCVI’.</p></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"202 ","pages":"Article 108053"},"PeriodicalIF":1.5,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122373","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: 0

On the use of the cumulant generating function for inference on time series 关于使用累积生成函数推断时间序列

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-28 DOI: 10.1016/j.csda.2024.108044

A. Moor, D. La Vecchia, E. Ronchetti

引用次数: 0

Minimax rates of convergence for sliced inverse regression with differential privacy 具有微分隐私的切片反回归的最小收敛率

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-22 DOI: 10.1016/j.csda.2024.108041

Wenbiao Zhao , Xuehu Zhu , Lixing Zhu

{"title":"Minimax rates of convergence for sliced inverse regression with differential privacy","authors":"Wenbiao Zhao , Xuehu Zhu , Lixing Zhu","doi":"10.1016/j.csda.2024.108041","DOIUrl":"10.1016/j.csda.2024.108041","url":null,"abstract":"<div><p>Sliced inverse regression (SIR) is a highly efficient paradigm used for the purpose of dimension reduction by replacing high-dimensional covariates with a limited number of linear combinations. This paper focuses on the implementation of the classical SIR approach integrated with a Gaussian differential privacy mechanism to estimate the central space while preserving privacy. We illustrate the tradeoff between statistical accuracy and privacy in sufficient dimension reduction problems under both the classical low- dimensional and modern high-dimensional settings. Additionally, we achieve the minimax rate of the proposed estimator with Gaussian differential privacy constraint and illustrate that this rate is also optimal for multiple index models with bounded dimension of the central space. Extensive numerical studies on synthetic data sets are conducted to assess the effectiveness of the proposed technique in finite sample scenarios, and a real data analysis is presented to showcase its practical application.</p></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"201 ","pages":"Article 108041"},"PeriodicalIF":1.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167947324001257/pdfft?md5=cab1d33929cc2c1071e939e0580ca683&pid=1-s2.0-S0167947324001257-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084124","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

Test for the mean of high-dimensional functional time series 高维函数时间序列均值检验

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-22 DOI: 10.1016/j.csda.2024.108040

Lin Yang , Zhenghui Feng , Qing Jiang

引用次数: 0

Community influence analysis in social networks 社交网络中的社区影响力分析

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-22 DOI: 10.1016/j.csda.2024.108037

Yuanxing Chen , Kuangnan Fang , Wei Lan , Chih-Ling Tsai , Qingzhao Zhang

引用次数: 0

Feasible model-based principal component analysis: Joint estimation of rank and error covariance matrix 基于模型的可行主成分分析：秩和误差协方差矩阵的联合估计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-22 DOI: 10.1016/j.csda.2024.108042

Tak-Shing T. Chan, Alex Gibberd

引用次数: 0

Hierarchical Bayesian spectral regression with shape constraints for multi-group data 针对多组数据的带形状约束的分层贝叶斯光谱回归

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-08-08 DOI: 10.1016/j.csda.2024.108036

Peter Lenk , Jangwon Lee , Dongu Han , Jichan Park , Taeryon Choi

{"title":"Hierarchical Bayesian spectral regression with shape constraints for multi-group data","authors":"Peter Lenk , Jangwon Lee , Dongu Han , Jichan Park , Taeryon Choi","doi":"10.1016/j.csda.2024.108036","DOIUrl":"10.1016/j.csda.2024.108036","url":null,"abstract":"<div><p>We propose a hierarchical Bayesian (HB) model for multi-group analysis with group–specific, flexible regression functions. The lower–level (within group) and upper–level (between groups) regression functions have hierarchical Gaussian process priors. HB smoothing priors are developed for the spectral coefficients. The HB priors smooth the estimated functions within and between groups. The HB model is particularly useful when data within groups are sparse because it shares information across groups, and provides more accurate estimates than fitting separate nonparametric models to each group. The proposed model also allows shape constraints, such as monotone, U and S–shaped, and multi-modal constraints. When appropriate, shape constraints improve estimation by recognizing violations of the shape constraints as noise. The model is illustrated by two examples: monotone growth curves for children, and happiness as a convex, U-shaped function of age in multiple countries. Various basis functions could also be used, and the paper also implements versions with B-splines and orthogonal polynomials.</p></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"200 ","pages":"Article 108036"},"PeriodicalIF":1.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979432","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: 0