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Journal of Nonparametric Statistics最新文献

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Nonparametric inference for interval data using kernel methods 用核方法对区间数据进行非参数推理
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2023-01-03 DOI: 10.1080/10485252.2022.2160980
Hoyoung Park, J. Loh, Woncheol Jang
{"title":"Nonparametric inference for interval data using kernel methods","authors":"Hoyoung Park, J. Loh, Woncheol Jang","doi":"10.1080/10485252.2022.2160980","DOIUrl":"https://doi.org/10.1080/10485252.2022.2160980","url":null,"abstract":"ABSTRACT Symbolic data have become increasingly popular in the era of big data. In this paper, we consider density estimation and regression for interval-valued data, a special type of symbolic data, common in astronomy and official statistics. We propose kernel estimators with adaptive bandwidths to account for variability of each interval. Specifically, we derive cross-validation bandwidth selectors for density estimation and extend the Nadaraya–Watson estimator for regression with interval data. We assess the performance of the proposed methods in comparison with existing kernel methods by extensive simulation studies and real data analysis.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"21 1","pages":"455 - 473"},"PeriodicalIF":1.2,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81559954","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
Testing axial symmetry by means of integrated rank scores 用综合等级分数检验轴对称性
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-12-26 DOI: 10.1080/10485252.2022.2159399
Š. Hudecová, M. Siman
{"title":"Testing axial symmetry by means of integrated rank scores","authors":"Š. Hudecová, M. Siman","doi":"10.1080/10485252.2022.2159399","DOIUrl":"https://doi.org/10.1080/10485252.2022.2159399","url":null,"abstract":"The article addresses the recently emerging inferential problem of testing axial symmetry up to a shift, which is useful even for testing certain hypotheses of exchangeability, independence, goodness-of-fit or equality of scale. In particular, it introduces a new test of axial symmetry based on integrated rank scores for directional quantile regression. The test outperforms existing competitors in terms of size, power, robustness, moment conditions or computational feasibility. All that is illustrated with a series of simulated examples.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"20 1","pages":"474 - 490"},"PeriodicalIF":1.2,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83047446","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
Maximum approximate Bernstein likelihood estimation in a two-sample semiparametric model 两样本半参数模型的最大近似Bernstein似然估计
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-12-21 DOI: 10.1080/10485252.2022.2158332
Zhong Guan
{"title":"Maximum approximate Bernstein likelihood estimation in a two-sample semiparametric model","authors":"Zhong Guan","doi":"10.1080/10485252.2022.2158332","DOIUrl":"https://doi.org/10.1080/10485252.2022.2158332","url":null,"abstract":"Maximum likelihood estimators are proposed for the parameters and the underlying densities in a semiparametric density ratio model in which the nonparametric baseline density is approximated by the Bernstein polynomial model. The EM algorithm is used to obtain the maximum approximate Bernstein likelihood estimates. The proposed method is illustrated by two real data from medical research and is shown by simulation to have better performance than the existing ones. Some asymptotic results are also presented and proved.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"7 1","pages":"437 - 453"},"PeriodicalIF":1.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87156439","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
Estimating POT second-order parameter for bias correction 估计偏置校正的POT二阶参数
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-12-16 DOI: 10.1080/10485252.2023.2226237
Nan Zou
{"title":"Estimating POT second-order parameter for bias correction","authors":"Nan Zou","doi":"10.1080/10485252.2023.2226237","DOIUrl":"https://doi.org/10.1080/10485252.2023.2226237","url":null,"abstract":"The stable tail dependence function provides a full characterization of the extremal dependence structures. Unfortunately, the estimation of the stable tail dependence function often suffers from significant bias, whose scale relates to the Peaks-Over-Threshold (POT) second-order parameter. For this second-order parameter, this paper introduces a penalized estimator that discourages it from being too close to zero. This paper then establishes this estimator's asymptotic consistency, uses it to correct the bias in the estimation of the stable tail dependence function, and illustrates its desirable empirical properties in the estimation of the extremal dependence structures.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"258 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77138146","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
Nonparametric inference about increasing odds rate distributions 关于增加的比值率分布的非参数推断
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-12-10 DOI: 10.1080/10485252.2023.2220050
T. Lando, Idir Arab, P. E. Oliveira
{"title":"Nonparametric inference about increasing odds rate distributions","authors":"T. Lando, Idir Arab, P. E. Oliveira","doi":"10.1080/10485252.2023.2220050","DOIUrl":"https://doi.org/10.1080/10485252.2023.2220050","url":null,"abstract":"To improve nonparametric estimates of lifetime distributions, we propose using the increasing odds rate (IOR) model as an alternative to other popular, but more restrictive, ``adverse ageing'' models, such as the increasing hazard rate one. This extends the scope of applicability of some methods for statistical inference under order restrictions, since the IOR model is compatible with heavy-tailed and bathtub distributions. We study a strongly uniformly consistent estimator of the cumulative distribution function of interest under the IOR constraint. Numerical evidence shows that this estimator often outperforms the classic empirical distribution function when the underlying model does belong to the IOR family. We also study two different tests, aimed at detecting deviations from the IOR property, and we establish their consistency. The performance of these tests is also evaluated through simulations.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"8 4 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78387434","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
Extended Glivenko–Cantelli theorem and L 1 strong consistency of innovation density estimator for time-varying semiparametric ARCH model 时变半参数ARCH模型创新密度估计的扩展Glivenko-Cantelli定理和L 1强相合性
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-12-05 DOI: 10.1080/10485252.2022.2152813
Chen Zhong
{"title":"Extended Glivenko–Cantelli theorem and L 1 strong consistency of innovation density estimator for time-varying semiparametric ARCH model","authors":"Chen Zhong","doi":"10.1080/10485252.2022.2152813","DOIUrl":"https://doi.org/10.1080/10485252.2022.2152813","url":null,"abstract":"ABSTRACT This paper extends the classical Glivenko–Cantelli theorem for the empirical cumulative distribution function based on the innovations in the ARCH model with a slowly time-varying trend. In this semiparametric time-varying model, strong consistency for the innovation density estimator via kernel smoothing method is established, given that the trend and ARCH parameter estimators meet some mild conditions. Besides, the strong consistency for the Gaussian quasi maximum likelihood estimator (QMLE) in the time-varying ARCH parameter is established as well. Moreover, in terms of the existence of the trend in the data, two major nonparametric trend estimators, B-spline and kernel estimators, are shown to be appropriate for the strong consistency results.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"99 1","pages":"373 - 396"},"PeriodicalIF":1.2,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83605981","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: 1
A nonparametric discontinuity test of density using a beta kernel 用beta核进行密度的非参数不连续检验
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-11-29 DOI: 10.1080/10485252.2022.2150766
Gaku Igarashi
{"title":"A nonparametric discontinuity test of density using a beta kernel","authors":"Gaku Igarashi","doi":"10.1080/10485252.2022.2150766","DOIUrl":"https://doi.org/10.1080/10485252.2022.2150766","url":null,"abstract":"In regression discontinuity design (RDD), the continuity of the density of a running variable is required. Hence, a discontinuity test of density is used for RDD. In previous studies, tests using difference estimators between the left- and right-hand limits of a density at a (potential) discontinuity point were suggested. In the present paper, a new discontinuity test based on direct density ratio estimation using a beta kernel is proposed. By using the ratio estimator in the proposed test statistic, rather than a difference estimator, the characteristic form of the asymptotic variance of the test statistic is obtained. Consequently, the power of the proposed test is shown to increase when used as a one-tailed test. Simulation studies illustrate the larger power of the proposed test when used as a one-tailed test.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"11 1","pages":"323 - 354"},"PeriodicalIF":1.2,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88283660","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
Nonparametric regression with responses missing at random and the scale depending on auxiliary covariates 随机缺失响应的非参数回归和依赖于辅助协变量的尺度
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-11-25 DOI: 10.1080/10485252.2022.2149749
Tian Jiang
{"title":"Nonparametric regression with responses missing at random and the scale depending on auxiliary covariates","authors":"Tian Jiang","doi":"10.1080/10485252.2022.2149749","DOIUrl":"https://doi.org/10.1080/10485252.2022.2149749","url":null,"abstract":"Nonparametric regression with missing at random (MAR) responses, univariate regression component of interest, and the scale function depending on both the predictor and auxiliary covariates, is considered. The asymptotic theory suggests that both heteroscedasticity and MAR mechanism affect the sharp constant of the minimax mean integrated squared error (MISE) convergence. Our sharp minimax procedure is based on the estimation of unknown nuisance scale function, design density and availability likelihood. The estimator is adaptive to the missing mechanism and unknown smoothness of the estimated regression function. Simulation studies and real examples also justify practical feasibility of the proposed method for this complex regression setting.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"4 1","pages":"302 - 322"},"PeriodicalIF":1.2,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81655138","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
Estimation of linear transformation cure models with informatively interval-censored failure time data 具有信息间隔截短失效时间数据的线性变换模型的估计
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-11-24 DOI: 10.1080/10485252.2022.2148667
Shuying Wang, Da Xu, Chunjie Wang, Jianguo Sun
{"title":"Estimation of linear transformation cure models with informatively interval-censored failure time data","authors":"Shuying Wang, Da Xu, Chunjie Wang, Jianguo Sun","doi":"10.1080/10485252.2022.2148667","DOIUrl":"https://doi.org/10.1080/10485252.2022.2148667","url":null,"abstract":"Linear transformation models have been one type of models commonly used for regression analysis of failure time data partly due to their flexibility. More recently they have been generalised to the case where there may exist a cured subgroup or the censoring may be informative. In this paper, we consider a more complicated and general situation where both a cured subgroup and informative censoring, or more specifically informative interval censoring, exist. As pointed out in the literature, the analysis that fails to take into account either the cured subgroup or the informative censoring can yield biased estimation or misleading conclusions. For the problem, a three-component mixture cure model is presented and we develop a two-step estimation procedure with the use of B-splines to approximate unknown functions. The proposed approach is quite flexible and can be easily implemented. Also the proposed estimators of regression parameters are shown to be consistent and asymptotically normal. An extensive simulation study is conducted and suggests that the method works well for practical situations. Furthermore a real application is provided to illustrate the proposed methodology.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"21 1","pages":"283 - 301"},"PeriodicalIF":1.2,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88217244","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: 1
The generalised MLE with truncated interval-censored data 截断间隔截尾数据的广义MLE
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2022-11-22 DOI: 10.1080/10485252.2022.2147173
Qiqing Yu
{"title":"The generalised MLE with truncated interval-censored data","authors":"Qiqing Yu","doi":"10.1080/10485252.2022.2147173","DOIUrl":"https://doi.org/10.1080/10485252.2022.2147173","url":null,"abstract":"The generalised maximum likelihood estimator (GMLE) of a survival function based on truncated interval-censored (TIC) data has been studied since 1990s (by Frydman, H. (1994), ‘A note on nonparametric estimation of the distribution function from interval censored and truncated data’, Journal of the Royal Statistical Society, Series B, 56, 71–74 among others). In the literature related to the GMLE based on TIC data, there are several issues that have not been properly settled in both methodology and theory including: (1) innermost intervals based on the TIC data are not correctly formulated and they lead to inconsistent estimators which are not the GMLE; and (2) the consistency of the GMLE has not been established. We settle these two issues in this paper. In particular, we specify the correct forms of innermost intervals and establish consistency results for the GMLE under a realistic model.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"73 1","pages":"266 - 282"},"PeriodicalIF":1.2,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82875942","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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