Pub Date : 2023-07-16DOI: 10.1080/10485252.2023.2235430
S. Jeong, Choongrak Kim, Hojin Yang
{"title":"Wasserstein filter for variable screening in binary classification in the reproducing kernel Hilbert space","authors":"S. Jeong, Choongrak Kim, Hojin Yang","doi":"10.1080/10485252.2023.2235430","DOIUrl":"https://doi.org/10.1080/10485252.2023.2235430","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"24 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73062450","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 : 2023-07-12DOI: 10.1080/10485252.2023.2234507
E. Bongiorno, Lax Chan, A. Goia
{"title":"Detecting the complexity of a functional time series","authors":"E. Bongiorno, Lax Chan, A. Goia","doi":"10.1080/10485252.2023.2234507","DOIUrl":"https://doi.org/10.1080/10485252.2023.2234507","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"14 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90174129","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 : 2023-07-10DOI: 10.1080/10485252.2023.2231565
C. Weiß, Alexander Schnurr
{"title":"Generalized ordinal patterns in discrete-valued time series: nonparametric testing for serial dependence","authors":"C. Weiß, Alexander Schnurr","doi":"10.1080/10485252.2023.2231565","DOIUrl":"https://doi.org/10.1080/10485252.2023.2231565","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"86 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78158366","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 : 2023-06-26DOI: 10.1080/10485252.2023.2220044
Lei Huang, Li Zhang, Yichuan Zhao
{"title":"Jackknife empirical likelihood for the lower-mean ratio","authors":"Lei Huang, Li Zhang, Yichuan Zhao","doi":"10.1080/10485252.2023.2220044","DOIUrl":"https://doi.org/10.1080/10485252.2023.2220044","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"31 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81186337","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 : 2023-06-15DOI: 10.1080/10485252.2023.2223322
Radhakanta Das
{"title":"An optimal sequential design in ethical allocation with an adaptive interim analysis","authors":"Radhakanta Das","doi":"10.1080/10485252.2023.2223322","DOIUrl":"https://doi.org/10.1080/10485252.2023.2223322","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"44 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82541296","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 : 2023-06-08DOI: 10.1080/10485252.2023.2219787
Feifei Chen, Wangxin Zhang, Zhihua Sun, Yu Guo
{"title":"Improved estimation of hazard function when failure information is missing not at random","authors":"Feifei Chen, Wangxin Zhang, Zhihua Sun, Yu Guo","doi":"10.1080/10485252.2023.2219787","DOIUrl":"https://doi.org/10.1080/10485252.2023.2219787","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"191 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72744944","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 : 2023-06-06DOI: 10.1080/10485252.2023.2219782
P. Bertail, M. Bouchouia, Ons Jelassi, J. Tressou, M. Zetlaoui
{"title":"Scaling by subsampling for big data, with applications to statistical learning","authors":"P. Bertail, M. Bouchouia, Ons Jelassi, J. Tressou, M. Zetlaoui","doi":"10.1080/10485252.2023.2219782","DOIUrl":"https://doi.org/10.1080/10485252.2023.2219782","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"170 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72792664","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 : 2023-05-30DOI: 10.1080/10485252.2023.2217941
Chun-Yen Wu, Li-Shan Huang, Zhezhen Jin
We consider local polynomial estimation for varying coefficient models and derive corresponding equivalent kernels that provide insights into the role of smoothing on the data and fill a gap in the literature. We show that the asymptotic equivalent kernels have an explicit decomposition with three parts: the inverse of the conditional moment matrix of covariates given the smoothing variable, the covariate vector, and the equivalent kernels of univariable local polynomials. We discuss finite-sample reproducing property which leads to zero bias in linear models with interactions between covariates and polynomials of the smoothing variable. By expressing the model in a centered form, equivalent kernels of estimating the intercept function are asymptotically identical to those of univariable local polynomials and estimators of slope functions are local analogues of slope estimators in linear models with weights assigned by equivalent kernels. Two examples are given to illustrate the weighting schemes and reproducing property.
{"title":"Decomposition and reproducing property of local polynomial equivalent kernels in varying coefficient models","authors":"Chun-Yen Wu, Li-Shan Huang, Zhezhen Jin","doi":"10.1080/10485252.2023.2217941","DOIUrl":"https://doi.org/10.1080/10485252.2023.2217941","url":null,"abstract":"We consider local polynomial estimation for varying coefficient models and derive corresponding equivalent kernels that provide insights into the role of smoothing on the data and fill a gap in the literature. We show that the asymptotic equivalent kernels have an explicit decomposition with three parts: the inverse of the conditional moment matrix of covariates given the smoothing variable, the covariate vector, and the equivalent kernels of univariable local polynomials. We discuss finite-sample reproducing property which leads to zero bias in linear models with interactions between covariates and polynomials of the smoothing variable. By expressing the model in a centered form, equivalent kernels of estimating the intercept function are asymptotically identical to those of univariable local polynomials and estimators of slope functions are local analogues of slope estimators in linear models with weights assigned by equivalent kernels. Two examples are given to illustrate the weighting schemes and reproducing property.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135690211","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 : 2023-05-26DOI: 10.1080/10485252.2023.2215341
Ruoxu Tan
We consider nonparametric regression where the covariate and the outcome variable are both subject to missingness. Previous work only discussed one of the variables that may be missing, but not both. Since missing at random is not an appropriate assumption in such a nonmonotone missing data context, we shall assume a missing not at random mechanism. We construct an inverse probability weighting local polynomial estimator based on a recently developed nonmonotone missing data model. It is well known that if the inverse probability weighting is too large at some fully observed cases, the resulting estimator would be deteriorated. To overcome this issue, we introduce a constrained maximum likelihood estimation and an estimating equations method to ensure that the resulting weighting is bounded. We prove the asymptotically normal result for the resulting regression estimator. Simulation results show good practical performance of our method. A real data example is also presented.
{"title":"Nonparametric regression with nonignorable missing covariates and outcomes using bounded inverse weighting","authors":"Ruoxu Tan","doi":"10.1080/10485252.2023.2215341","DOIUrl":"https://doi.org/10.1080/10485252.2023.2215341","url":null,"abstract":"We consider nonparametric regression where the covariate and the outcome variable are both subject to missingness. Previous work only discussed one of the variables that may be missing, but not both. Since missing at random is not an appropriate assumption in such a nonmonotone missing data context, we shall assume a missing not at random mechanism. We construct an inverse probability weighting local polynomial estimator based on a recently developed nonmonotone missing data model. It is well known that if the inverse probability weighting is too large at some fully observed cases, the resulting estimator would be deteriorated. To overcome this issue, we introduce a constrained maximum likelihood estimation and an estimating equations method to ensure that the resulting weighting is bounded. We prove the asymptotically normal result for the resulting regression estimator. Simulation results show good practical performance of our method. A real data example is also presented.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"329 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80468370","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：