Pub Date : 2024-07-24DOI: 10.1080/10485252.2024.2378897
Jeong Min Jeon
In this paper, we explore a novel regression problem encompassing both Euclidean and non-Euclidean predictors, all of which are subject to measurement errors. Specifically, we focus on a non-Euclid...
{"title":"Errors-in-variables regression for mixed Euclidean and non-Euclidean predictors","authors":"Jeong Min Jeon","doi":"10.1080/10485252.2024.2378897","DOIUrl":"https://doi.org/10.1080/10485252.2024.2378897","url":null,"abstract":"In this paper, we explore a novel regression problem encompassing both Euclidean and non-Euclidean predictors, all of which are subject to measurement errors. Specifically, we focus on a non-Euclid...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"20 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776266","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}
Pub Date : 2024-07-24DOI: 10.1080/10485252.2024.2378904
Yong Wang, Reza Modarres
We present a novel clustering method for high-dimensional, low sample size (HDLSS) data. The method is distance-based, takes advantage of the distance concentration phenomenon and the limiting valu...
{"title":"Clustering of high-dimensional observations","authors":"Yong Wang, Reza Modarres","doi":"10.1080/10485252.2024.2378904","DOIUrl":"https://doi.org/10.1080/10485252.2024.2378904","url":null,"abstract":"We present a novel clustering method for high-dimensional, low sample size (HDLSS) data. The method is distance-based, takes advantage of the distance concentration phenomenon and the limiting valu...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"38 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141776149","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}
Pub Date : 2024-07-09DOI: 10.1080/10485252.2024.2368631
Enze Shi, Jinhan Xie, Shenggang Hu, Ke Sun, Hongsheng Dai, Bei Jiang, Linglong Kong, Lingzhu Li
The rapid growth of data volume and velocity is challenging traditional methods of classification, making it impossible to store so much data in memory. Developing online classification methods is ...
{"title":"Tracking full posterior in online Bayesian classification learning: a particle filter approach","authors":"Enze Shi, Jinhan Xie, Shenggang Hu, Ke Sun, Hongsheng Dai, Bei Jiang, Linglong Kong, Lingzhu Li","doi":"10.1080/10485252.2024.2368631","DOIUrl":"https://doi.org/10.1080/10485252.2024.2368631","url":null,"abstract":"The rapid growth of data volume and velocity is challenging traditional methods of classification, making it impossible to store so much data in memory. Developing online classification methods is ...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"30 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141608361","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}
Pub Date : 2024-07-03DOI: 10.1080/10485252.2024.2371524
Dongxiao Han, Miao Han, Meiling Hao, Liuquan Sun, Siyang Wang
For the supervised and semi-supervised settings, a group inference method is proposed for regression parameters in high-dimensional semi-parametric single-index models with an unknown random link f...
{"title":"Group inference of high-dimensional single-index models","authors":"Dongxiao Han, Miao Han, Meiling Hao, Liuquan Sun, Siyang Wang","doi":"10.1080/10485252.2024.2371524","DOIUrl":"https://doi.org/10.1080/10485252.2024.2371524","url":null,"abstract":"For the supervised and semi-supervised settings, a group inference method is proposed for regression parameters in high-dimensional semi-parametric single-index models with an unknown random link f...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"78 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141608359","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}
Pub Date : 2024-06-18DOI: 10.1080/10485252.2024.2366978
Daoji Li, Yinfei Kong, Dawit Zerom
In practical applications, one often does not know the ‘true’ structure of the underlying conditional quantile function, especially in the ultra-high dimensional setting. To deal with ultra-high di...
{"title":"Nonparametric screening for additive quantile regression in ultra-high dimension","authors":"Daoji Li, Yinfei Kong, Dawit Zerom","doi":"10.1080/10485252.2024.2366978","DOIUrl":"https://doi.org/10.1080/10485252.2024.2366978","url":null,"abstract":"In practical applications, one often does not know the ‘true’ structure of the underlying conditional quantile function, especially in the ultra-high dimensional setting. To deal with ultra-high di...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"31 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529569","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}
Pub Date : 2024-05-27DOI: 10.1080/10485252.2024.2358435
Chunxi Liu, Chao Han, Weiping Zhang
In this paper, we propose a penalized regression method to detect subgroups of trajectories while accounting for the time-varying effects of given covariates. Specifically, we allow both the latent...
{"title":"Trajectory clustering with adjustment for time-varying covariate effects","authors":"Chunxi Liu, Chao Han, Weiping Zhang","doi":"10.1080/10485252.2024.2358435","DOIUrl":"https://doi.org/10.1080/10485252.2024.2358435","url":null,"abstract":"In this paper, we propose a penalized regression method to detect subgroups of trajectories while accounting for the time-varying effects of given covariates. Specifically, we allow both the latent...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"67 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507293","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}
Pub Date : 2024-05-24DOI: 10.1080/10485252.2024.2359057
Lizhe Sun, Mingyuan Wang, Siquan Zhu, Adrian Barbu
Current online learning methods suffer issues such as lower convergence rates and limited capability to select important features compared to their offline counterparts. In this paper, a novel fram...
{"title":"A novel framework for online supervised learning with feature selection","authors":"Lizhe Sun, Mingyuan Wang, Siquan Zhu, Adrian Barbu","doi":"10.1080/10485252.2024.2359057","DOIUrl":"https://doi.org/10.1080/10485252.2024.2359057","url":null,"abstract":"Current online learning methods suffer issues such as lower convergence rates and limited capability to select important features compared to their offline counterparts. In this paper, a novel fram...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"40 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529570","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}
Pub Date : 2024-05-03DOI: 10.1080/10485252.2024.2348542
L. Grammont, H. Maatouk, X. Bay
In this paper, we extend the correspondence between Bayesian estimation and optimal smoothing in a Reproducing Kernel Hilbert Space (RKHS) by adding convex constraints to the problem. Through a seq...
{"title":"Equivalence between constrained optimal smoothing and Bayesian estimation","authors":"L. Grammont, H. Maatouk, X. Bay","doi":"10.1080/10485252.2024.2348542","DOIUrl":"https://doi.org/10.1080/10485252.2024.2348542","url":null,"abstract":"In this paper, we extend the correspondence between Bayesian estimation and optimal smoothing in a Reproducing Kernel Hilbert Space (RKHS) by adding convex constraints to the problem. Through a seq...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"35 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929621","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}
Pub Date : 2024-04-03DOI: 10.1080/10485252.2024.2335494
Jianhua Zhou, Christopher F. Parmeter
We investigate data-driven bandwidth selection within the confines of robust (resistant) kernel smoothing. While several approaches presently exist, they require user defined robustness parameters....
{"title":"Data-driven resistant kernel regression","authors":"Jianhua Zhou, Christopher F. Parmeter","doi":"10.1080/10485252.2024.2335494","DOIUrl":"https://doi.org/10.1080/10485252.2024.2335494","url":null,"abstract":"We investigate data-driven bandwidth selection within the confines of robust (resistant) kernel smoothing. While several approaches presently exist, they require user defined robustness parameters....","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"25 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140576413","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}
Pub Date : 2024-03-12DOI: 10.1080/10485252.2024.2328078
Junyi Zhang, Ao Yuan, Ming T. Tan
For observational studies or clinical trials not fully randomised, the baseline covariates are often not balanced between the treatment and control groups. In this case, the traditional estimates o...
{"title":"Enhanced doubly robust estimation with concave link functions for estimands in clinical trials","authors":"Junyi Zhang, Ao Yuan, Ming T. Tan","doi":"10.1080/10485252.2024.2328078","DOIUrl":"https://doi.org/10.1080/10485252.2024.2328078","url":null,"abstract":"For observational studies or clinical trials not fully randomised, the baseline covariates are often not balanced between the treatment and control groups. In this case, the traditional estimates o...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"131 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148493","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}
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