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}
Pub Date : 2023-05-22DOI: 10.1080/10485252.2023.2215339
Jianan Chen, Binyan Jiang, Jialiang Li
We present a new nonparametric model averaging approach to the instrumental variable (IV) regression where the effects of multiple instruments on the endogenous variable are modelled as nonparametric functions in the reduced form equations. Even if individual IVs may have weak and nonlinear relevance to the exposure, our proposed model averaging is able to ensemble their effects with optimal weights to produce valid inference. Our analysis covers both the case in which the number of IV is fixed and the case in which the dimension of IV is diverging with sample size. This novel framework can be especially beneficial to the practical situations involving weak IVs since in many recent observational studies we may encounter a large number of instruments and their quality could range from poor to strong. Numerical studies are carried out and comparisons are made between our proposed method and a wide range of existing alternative methods.
{"title":"Nonparametric instrument model averaging","authors":"Jianan Chen, Binyan Jiang, Jialiang Li","doi":"10.1080/10485252.2023.2215339","DOIUrl":"https://doi.org/10.1080/10485252.2023.2215339","url":null,"abstract":"We present a new nonparametric model averaging approach to the instrumental variable (IV) regression where the effects of multiple instruments on the endogenous variable are modelled as nonparametric functions in the reduced form equations. Even if individual IVs may have weak and nonlinear relevance to the exposure, our proposed model averaging is able to ensemble their effects with optimal weights to produce valid inference. Our analysis covers both the case in which the number of IV is fixed and the case in which the dimension of IV is diverging with sample size. This novel framework can be especially beneficial to the practical situations involving weak IVs since in many recent observational studies we may encounter a large number of instruments and their quality could range from poor to strong. Numerical studies are carried out and comparisons are made between our proposed method and a wide range of existing alternative methods.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"12 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76464275","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 : 2023-05-10DOI: 10.1080/10485252.2023.2210686
Weiwei Zhuang, Suming Yao, Guoxin Qiu
New test statistics for weakly Lorenz dominance and weakly generalised Lorenz dominance are proposed, and some asymptotic properties of test statistics are obtained. The simulation results show that our test statistics can improve test power in comparison with the non-smoothed empirical methods. Finally, we apply our inference framework to an actual example.
{"title":"Test of dominance relations based on kernel smoothing method","authors":"Weiwei Zhuang, Suming Yao, Guoxin Qiu","doi":"10.1080/10485252.2023.2210686","DOIUrl":"https://doi.org/10.1080/10485252.2023.2210686","url":null,"abstract":"New test statistics for weakly Lorenz dominance and weakly generalised Lorenz dominance are proposed, and some asymptotic properties of test statistics are obtained. The simulation results show that our test statistics can improve test power in comparison with the non-smoothed empirical methods. Finally, we apply our inference framework to an actual example.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"45 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78967286","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 : 2023-04-24DOI: 10.1080/10485252.2023.2203275
Lei Ge, Jai H. Choi, Hui Zhao, Yang Li, Jianguo Sun
Event history data commonly occur in many areas and a great deal of literature on their analysis has been established. However, most of the existing methods apply only to a single type of event history data. Recently, several authors have discussed the analysis of mixed types of event history data and the existence of dependent observation processes is another issue that one often has to deal with in the analysis of event history data. This paper discusses regression analysis of mixed panel count data with dependent observation processes, which has not been addressed in the literature, and for the problem, an approximate likelihood estimation approach is proposed. For the implementation, an EM algorithm is developed and the proposed estimators are shown to be consistent and asymptotically normal. An extensive simulation study is performed to assess the performance of the proposed approach and indicates that it works well in practical situations. An application to a set of real data is provided.
{"title":"Regression analysis of mixed panel count data with dependent observation processes","authors":"Lei Ge, Jai H. Choi, Hui Zhao, Yang Li, Jianguo Sun","doi":"10.1080/10485252.2023.2203275","DOIUrl":"https://doi.org/10.1080/10485252.2023.2203275","url":null,"abstract":"Event history data commonly occur in many areas and a great deal of literature on their analysis has been established. However, most of the existing methods apply only to a single type of event history data. Recently, several authors have discussed the analysis of mixed types of event history data and the existence of dependent observation processes is another issue that one often has to deal with in the analysis of event history data. This paper discusses regression analysis of mixed panel count data with dependent observation processes, which has not been addressed in the literature, and for the problem, an approximate likelihood estimation approach is proposed. For the implementation, an EM algorithm is developed and the proposed estimators are shown to be consistent and asymptotically normal. An extensive simulation study is performed to assess the performance of the proposed approach and indicates that it works well in practical situations. An application to a set of real data is provided.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"58 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78160608","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 : 2023-04-14DOI: 10.1080/10485252.2023.2197088
Xiaomeng Qi, Zhangsheng Yu
We study the local kernel pseudo-partial likelihood approach for the cause-specific hazard model with nonparametric covariate functions. The derivative of the covariate function is estimated first, and the estimator of the nonparametric covariate function is then derived by integrating the derivative estimator. The consistency and pointwise asymptotic normality of the local kernel estimator for the interested failure types are obtained. Moreover, numerical studies show that the proposed kernel estimator performs well under a finite sample size. And we compare the local kernel estimator with the regression B-splines estimator. We also apply the proposed method to analyse the kidney and renal pelvis cancer data with composite endpoints.
{"title":"Kernel regression for cause-specific hazard models with nonparametric covariate functions","authors":"Xiaomeng Qi, Zhangsheng Yu","doi":"10.1080/10485252.2023.2197088","DOIUrl":"https://doi.org/10.1080/10485252.2023.2197088","url":null,"abstract":"We study the local kernel pseudo-partial likelihood approach for the cause-specific hazard model with nonparametric covariate functions. The derivative of the covariate function is estimated first, and the estimator of the nonparametric covariate function is then derived by integrating the derivative estimator. The consistency and pointwise asymptotic normality of the local kernel estimator for the interested failure types are obtained. Moreover, numerical studies show that the proposed kernel estimator performs well under a finite sample size. And we compare the local kernel estimator with the regression B-splines estimator. We also apply the proposed method to analyse the kidney and renal pelvis cancer data with composite endpoints.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"13 1","pages":"642 - 667"},"PeriodicalIF":1.2,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79595356","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 : 2023-03-16DOI: 10.1080/10485252.2023.2185749
Marija Cuparić, B. Milošević
{"title":"IPCW approach for testing independence","authors":"Marija Cuparić, B. Milošević","doi":"10.1080/10485252.2023.2185749","DOIUrl":"https://doi.org/10.1080/10485252.2023.2185749","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"11 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85767504","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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