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}
Pub Date : 2024-03-12DOI: 10.1080/10485252.2024.2324290
Masaru Hashimoto, Peter J. Lenk
Marked point processes provide a flexible framework for studying ultra-high frequency financial data that records the time and price for each transaction. This paper estimates compound, inhomogeneo...
{"title":"Bayesian semi-parametric estimation of compound inhomogeneous Poisson processes for ultra-high frequency financial transaction data","authors":"Masaru Hashimoto, Peter J. Lenk","doi":"10.1080/10485252.2024.2324290","DOIUrl":"https://doi.org/10.1080/10485252.2024.2324290","url":null,"abstract":"Marked point processes provide a flexible framework for studying ultra-high frequency financial data that records the time and price for each transaction. This paper estimates compound, inhomogeneo...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"43 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140127360","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 : 2024-03-07DOI: 10.1080/10485252.2024.2320801
Yaxuan Zhao, Yuehan Yang
To effectively handle functional data and longitudinal data, we propose a robust estimation approach based on penalised regularisation with the framework of the varying-coefficient additive model. ...
{"title":"A robust estimation based on penalised regularisation for the varying-coefficient additive model","authors":"Yaxuan Zhao, Yuehan Yang","doi":"10.1080/10485252.2024.2320801","DOIUrl":"https://doi.org/10.1080/10485252.2024.2320801","url":null,"abstract":"To effectively handle functional data and longitudinal data, we propose a robust estimation approach based on penalised regularisation with the framework of the varying-coefficient additive model. ...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"12 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140127056","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 : 2024-03-05DOI: 10.1080/10485252.2024.2320798
Mengyao Li, Jiangshe Zhang, Jun Zhang, Yan Zhou
We study the goodness-of-fit tests for checking the normality of the model errors under the additive distortion measurement error settings. Neither the response variable nor the covariates can be d...
{"title":"Checking normality of model errors under additive distortion measurement errors","authors":"Mengyao Li, Jiangshe Zhang, Jun Zhang, Yan Zhou","doi":"10.1080/10485252.2024.2320798","DOIUrl":"https://doi.org/10.1080/10485252.2024.2320798","url":null,"abstract":"We study the goodness-of-fit tests for checking the normality of the model errors under the additive distortion measurement error settings. Neither the response variable nor the covariates can be d...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"104 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140033848","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 : 2024-03-05DOI: 10.1080/10485252.2024.2320128
Rafael Weißbach, Lucas Radloff, Constantin Reinke, G. Doblhammer
A continuous-time multi-state history is semi-Markovian, if an intensity to migrate from one state into a second, depends on the duration in the first state. Such duration can be formalised as mark...
{"title":"A powerful nonparametric test of the effect of dementia duration on mortality","authors":"Rafael Weißbach, Lucas Radloff, Constantin Reinke, G. Doblhammer","doi":"10.1080/10485252.2024.2320128","DOIUrl":"https://doi.org/10.1080/10485252.2024.2320128","url":null,"abstract":"A continuous-time multi-state history is semi-Markovian, if an intensity to migrate from one state into a second, depends on the duration in the first state. Such duration can be formalised as mark...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"267 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140033868","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 : 2024-02-19DOI: 10.1080/10485252.2024.2313137
Hsin-Hsiung Huang, Feng Yu, Teng Zhang
We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using α-distance covariance (dCov) in dimension-reduction problems. Under very mild conditions on t...
{"title":"Robust sufficient dimension reduction via α-distance covariance","authors":"Hsin-Hsiung Huang, Feng Yu, Teng Zhang","doi":"10.1080/10485252.2024.2313137","DOIUrl":"https://doi.org/10.1080/10485252.2024.2313137","url":null,"abstract":"We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using α-distance covariance (dCov) in dimension-reduction problems. Under very mild conditions on t...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"10 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139911250","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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