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Variable selection for multivariate functional data via conditional correlation learning 通过条件相关学习为多元函数数据选择变量
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-14 DOI: 10.1007/s00180-024-01489-y
Keyao Wang, Huiwen Wang, Shanshan Wang, Lihong Wang
{"title":"Variable selection for multivariate functional data via conditional correlation learning","authors":"Keyao Wang, Huiwen Wang, Shanshan Wang, Lihong Wang","doi":"10.1007/s00180-024-01489-y","DOIUrl":"https://doi.org/10.1007/s00180-024-01489-y","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140705969","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
Closed-form expressions of the run-length distribution of the nonparametric double sampling precedence monitoring scheme 非参数双抽样优先监测方案运行长度分布的闭式表达式
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-12 DOI: 10.1007/s00180-024-01488-z
Zwelakhe Magagula, J. Malela‐Majika, S. Human, Philippe Castagliola, Kashinath Chatterjee, Christos Koukouvinos
{"title":"Closed-form expressions of the run-length distribution of the nonparametric double sampling precedence monitoring scheme","authors":"Zwelakhe Magagula, J. Malela‐Majika, S. Human, Philippe Castagliola, Kashinath Chatterjee, Christos Koukouvinos","doi":"10.1007/s00180-024-01488-z","DOIUrl":"https://doi.org/10.1007/s00180-024-01488-z","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711693","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 simple algorithm for computing the probabilities of count models based on pure birth processes 计算基于纯出生过程的计数模型概率的简单算法
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-10 DOI: 10.1007/s00180-024-01491-4
Mongkol Hunkrajok, Wanrudee Skulpakdee
{"title":"A simple algorithm for computing the probabilities of count models based on pure birth processes","authors":"Mongkol Hunkrajok, Wanrudee Skulpakdee","doi":"10.1007/s00180-024-01491-4","DOIUrl":"https://doi.org/10.1007/s00180-024-01491-4","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140720137","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
Maximizing adjusted covariance: new supervised dimension reduction for classification 调整后协方差最大化：用于分类的新监督降维方法
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-02 DOI: 10.1007/s00180-024-01472-7
Hyejoon Park, Hyunjoong Kim, Yung-Seop Lee
{"title":"Maximizing adjusted covariance: new supervised dimension reduction for classification","authors":"Hyejoon Park, Hyunjoong Kim, Yung-Seop Lee","doi":"10.1007/s00180-024-01472-7","DOIUrl":"https://doi.org/10.1007/s00180-024-01472-7","url":null,"abstract":"<p>This study proposes a new linear dimension reduction technique called Maximizing Adjusted Covariance (MAC), which is suitable for supervised classification. The new approach is to adjust the covariance matrix between input and target variables using the within-class sum of squares, thereby promoting class separation after linear dimension reduction. MAC has a low computational cost and can complement existing linear dimensionality reduction techniques for classification. In this study, the classification performance by MAC was compared with those of the existing linear dimension reduction methods using 44 datasets. In most of the classification models used in the experiment, the MAC dimension reduction method showed better classification accuracy and F1 score than other linear dimension reduction methods.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567927","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 class of transformed joint quantile time series models with applications to health studies 一类转化联合量化时间序列模型在健康研究中的应用
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-04-01 DOI: 10.1007/s00180-024-01484-3
Fahimeh Tourani-Farani, Zeynab Aghabazaz, Iraj Kazemi
{"title":"A class of transformed joint quantile time series models with applications to health studies","authors":"Fahimeh Tourani-Farani, Zeynab Aghabazaz, Iraj Kazemi","doi":"10.1007/s00180-024-01484-3","DOIUrl":"https://doi.org/10.1007/s00180-024-01484-3","url":null,"abstract":"<p>Extensions of quantile regression modeling for time series analysis are extensively employed in medical and health studies. This study introduces a specific class of transformed quantile-dispersion regression models for non-stationary time series. These models possess the flexibility to incorporate the time-varying structure into the model specification, enabling precise predictions for future decisions. Our proposed modeling methodology applies to dynamic processes characterized by high variation and possible periodicity, relying on a non-linear framework. Additionally, unlike the transformed time series model, our approach directly interprets the regression parameters concerning the initial response. For computational purposes, we present an iteratively reweighted least squares algorithm. To assess the performance of our model, we conduct simulation experiments. To illustrate the modeling strategy, we analyze time-series measurements of influenza infection and daily COVID-19 deaths.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140567967","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
Modal regression models based on B-splines 基于 B 样条的模态回归模型
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-03-31 DOI: 10.1007/s00180-024-01487-0
Lianqiang Yang, Wanli Yuan, Shijie Wang
{"title":"Modal regression models based on B-splines","authors":"Lianqiang Yang, Wanli Yuan, Shijie Wang","doi":"10.1007/s00180-024-01487-0","DOIUrl":"https://doi.org/10.1007/s00180-024-01487-0","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360190","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
Machine learning embedded EM algorithms for semiparametric mixture regression models 半参数混合回归模型的机器学习嵌入式 EM 算法
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-03-31 DOI: 10.1007/s00180-024-01482-5
Jiacheng Xue, Weixin Yao, Sijia Xiang
{"title":"Machine learning embedded EM algorithms for semiparametric mixture regression models","authors":"Jiacheng Xue, Weixin Yao, Sijia Xiang","doi":"10.1007/s00180-024-01482-5","DOIUrl":"https://doi.org/10.1007/s00180-024-01482-5","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140360818","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
An attribute-based Node2Vec model for dynamic community detection on co-authorship network 基于属性的 Node2Vec 模型用于共同著作网络的动态社区检测
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-03-29 DOI: 10.1007/s00180-024-01486-1
Tong Zhou, Rui Pan, Junfei Zhang, Hansheng Wang
{"title":"An attribute-based Node2Vec model for dynamic community detection on co-authorship network","authors":"Tong Zhou, Rui Pan, Junfei Zhang, Hansheng Wang","doi":"10.1007/s00180-024-01486-1","DOIUrl":"https://doi.org/10.1007/s00180-024-01486-1","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367368","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 difference-based method for testing no effect in nonparametric regression 基于差异的非参数回归无效应检验方法
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-03-27 DOI: 10.1007/s00180-024-01479-0
Zhijian Li, Tiejun Tong, Yuedong Wang
{"title":"A difference-based method for testing no effect in nonparametric regression","authors":"Zhijian Li, Tiejun Tong, Yuedong Wang","doi":"10.1007/s00180-024-01479-0","DOIUrl":"https://doi.org/10.1007/s00180-024-01479-0","url":null,"abstract":"","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375129","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 smoothed semiparametric likelihood for estimation of nonparametric finite mixture models with a copula-based dependence structure 用于估计具有基于共轭依赖结构的非参数有限混合物模型的平滑半参数似然法
IF 1.3 4区 数学
Computational Statistics Pub Date : 2024-03-27 DOI: 10.1007/s00180-024-01483-4
Michael Levine, Gildas Mazo
{"title":"A smoothed semiparametric likelihood for estimation of nonparametric finite mixture models with a copula-based dependence structure","authors":"Michael Levine, Gildas Mazo","doi":"10.1007/s00180-024-01483-4","DOIUrl":"https://doi.org/10.1007/s00180-024-01483-4","url":null,"abstract":"<p>In this manuscript, we consider a finite multivariate nonparametric mixture model where the dependence between the marginal densities is modeled using the copula device. Pseudo expectation–maximization (EM) stochastic algorithms were recently proposed to estimate all of the components of this model under a location-scale constraint on the marginals. Here, we introduce a deterministic algorithm that seeks to maximize a smoothed semiparametric likelihood. No location-scale assumption is made about the marginals. The algorithm is monotonic in one special case, and, in another, leads to “approximate monotonicity”—whereby the difference between successive values of the objective function becomes non-negative up to an additive term that becomes negligible after a sufficiently large number of iterations. The behavior of this algorithm is illustrated on several simulated and real datasets. The results suggest that, under suitable conditions, the proposed algorithm may indeed be monotonic in general. A discussion of the results and some possible future research directions round out our presentation.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884878","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
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