Journal of Nonparametric Statistics最新文献

{"title":"A bootstrap functional central limit theorem for time-varying linear processes","authors":"Carina Beering, Anne Leucht","doi":"10.1080/10485252.2023.2284896","DOIUrl":"https://doi.org/10.1080/10485252.2023.2284896","url":null,"abstract":"We provide a functional central limit theorem for a broad class of smooth functions for possibly non-causal multivariate linear processes with time-varying coefficients. Since the limiting processe...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"93 ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518667","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}

{"title":"Integrated log-rank test","authors":"John O'Quigley","doi":"10.1080/10485252.2023.2284897","DOIUrl":"https://doi.org/10.1080/10485252.2023.2284897","url":null,"abstract":"The log-rank test can be viewed as nonparametric from the standpoint of a series of 2×2 tables, as semi-parametric from the standpoint of the proportional hazards model and as parametric from the v...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"94 ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518670","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}

{"title":"Home-range estimation under a restricted sample scheme","authors":"Alejandro Cholaquidis, Ricardo Fraiman, Manuel Hernández-Banadik","doi":"10.1080/10485252.2023.2280003","DOIUrl":"https://doi.org/10.1080/10485252.2023.2280003","url":null,"abstract":"AbstractNew continuous-time models and statistical methods have been developed to estimate some sets related to animal movement, such as the home-range and the core-area among others, when the information of the trajectory is provided by a GPS. Because data transfer costs and GPS battery life are practical constraints, the experimental designer must make critical sampling decisions to maximise information. We introduce the on–off sampling scheme, where the GPS is alternately on and off. This scheme is already used in practice but with insufficient statistical theoretical support. We prove the consistency of home-range estimators with an underlying reflected diffusion model under this sampling method. The same rate of convergence is achieved as in the case where the GPS is always on for the whole experiment. This is illustrated by a simulation study and real data. We also provide estimators of the stationary distribution, its level sets and the drift function.Keywords: Home-range estimationreflected Brownian motion with driftstationarity distributionlevel set estimation2010 Mathematics Subject Classifications: 62M2062G2060J70 AcknowledgmentsWe thanks Dr. Stephen Blake, of the Max Planck Institute for Ornithology, for facilitating access to the data set that was used in this paper. The data that support the findings of this study are openly available in Movebank at https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1818825, reference number 1818825.We thanks the editor and three referee's for their constructive comments which improves significantly the present version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by grants ANII (Agencia Nacional de Investigación e Innovación) [grant numbers POSNAC20191157608, FCE120191156054].","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"75 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092733","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}

{"title":"Regression analysis of multivariate interval-censored failure time data with a cured subgroup and informative censoring","authors":"Mingyue Du, Mengzhu Yu","doi":"10.1080/10485252.2023.2280016","DOIUrl":"https://doi.org/10.1080/10485252.2023.2280016","url":null,"abstract":"AbstractMultivariate interval-censored failure time data occur when a failure time study involves several related failure times of interest and only interval-censored observations are available for each of them. Although a great deal of literature has been established for their regression analysis, there does not seem to exist an approach that applies to the situation where there exist both a cured subgroup and informative censoring, the focus of this paper. For the problem, a class of semiparametric transformation non-mixture cure models is presented and a two-step estimation procedure is proposed. For the implementation of the proposed method, an EM algorithm is developed. Numerical results suggest that the proposed method works well for practical situations and an application is provided.Keywords: Informative censoringmultivariate interval-censored datanon-mixture cure modeltransformation model AcknowledgementsThe authors wish to thank the Editor, Prof. Wenbin Lu, the Associate Editor and two reviewers for their helpful comments and suggestions that greatly improved the paper. The R code for the implementation of the proposed method is available from the second author upon request.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"47 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092617","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}

{"title":"Complete <i>f</i> -moment convergence for <i>m</i> -asymptotic negatively associated random variables and related statistical applications","authors":"Xuejun Wang, Xi Chen, Tien-Chung Hu, Andrei Volodin","doi":"10.1080/10485252.2023.2280004","DOIUrl":"https://doi.org/10.1080/10485252.2023.2280004","url":null,"abstract":"AbstractIn this article, the complete f-moment convergence for m-asymptotic negatively associated random variables is investigated. As applications, we establish the strong consistency of the least square estimator in the simple linear errors-in-variables models and the complete consistency for estimator in the semiparametric regression model based on m-asymptotic negatively associated errors. We also give some simulations to assess the finite sample performance of the theoretical results.Keywords: m-Asymptotic negatively associated random variablescomplete f-moment convergenceconsistencyerrors-in-variables modelssemiparametric regression modelsMathematics Subject Classifications: 60F1562G20 AcknowledgmentsThe authors are most grateful to the Editor and anonymous referee for carefully reading the manuscript and valuable suggestions which helped in improving an earlier version of this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingSupported by the National Social Science Foundation of China (22BTJ059).","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":" 94","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241576","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}

{"title":"Persistent homology based goodness-of-fit tests for spatial tessellations","authors":"Christian Hirsch, Johannes Krebs, Claudia Redenbach","doi":"10.1080/10485252.2023.2280022","DOIUrl":"https://doi.org/10.1080/10485252.2023.2280022","url":null,"abstract":"AbstractMotivated by the rapidly increasing relevance of virtual material design in the domain of materials science, it has become essential to assess whether topological properties of stochastic models for a spatial tessellation are in accordance with a given dataset. Recently, tools from topological data analysis such as the persistence diagram have allowed to reach profound insights in a variety of application contexts. In this work, we establish the asymptotic normality of a variety of test statistics derived from a tessellation-adapted refinement of the persistence diagram. Since in applications, it is common to work with tessellation data subject to interactions, we establish our main results for Voronoi and Laguerre tessellations whose generators form a Gibbs point process. We elucidate how these conceptual results can be used to derive goodness of fit tests, and then investigate their power in a simulation study. Finally, we apply our testing methodology to a tessellation describing real foam data.Keywords: Tessellationtopological data analysisgoodness-of-fitpersistence diagram2010 Mathematics Subject Classifications: 60K3560F1082C22 AcknowledgmentsWe thank the two anonymous referees for their careful reading of the manuscript. Their comments and suggestions substantially improved the quality of the presentation. We thank Anne Jung (Helmut Schmidt University Hamburg) for providing the foam sample and Christian Jung (RPTU Kaiserslautern-Landau) for computing the Laguerre approximation.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingJohannes Krebs was partially supported by the German Research Foundation (DFG), Grant Number KR-4977/2-1.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241575","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}

{"title":"Approximate tolerance intervals for nonparametric regression models","authors":"Yafan Guo, Derek S. Young","doi":"10.1080/10485252.2023.2277260","DOIUrl":"https://doi.org/10.1080/10485252.2023.2277260","url":null,"abstract":"AbstractTolerance intervals in regression allow the user to quantify, with a specified degree of confidence, bounds for a specified proportion of the sampled population when conditioned on a set of covariate values. While methods are available for tolerance intervals in fully-parametric regression settings, the construction of tolerance intervals for nonparametric regression models has been treated in a limited capacity. This paper fills this gap and develops likelihood-based approaches for the construction of pointwise one-sided and two-sided tolerance intervals for nonparametric regression models. A numerical approach is also presented for constructing simultaneous tolerance intervals. An appealing facet of this work is that the resulting methodology is consistent with what is done for fully-parametric regression tolerance intervals. Extensive coverage studies are presented, which demonstrate very good performance of the proposed methods. The proposed tolerance intervals are calculated and interpreted for analyses involving a fertility dataset and a triceps measurement dataset.Keywords: Bootstrapboundary effectscoverage probabilitiesk-factorsmoothing splineAMS Subject Classifications: 62G0862G15 AcknowledgmentsWe would thank the University of Kentucky Center for Computational Sciences and Information Technology Services Research Computing for their support and use of the Lipscomb Compute Cluster and associated research computing resources. The authors are also thankful to the Associate Editor and two reviewers who provided numerous insightful comments that improved the overall quality of this work.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe fertility data are available at the HFC's website bluehttps://www.fertilitydata.org/cgi-bin/data.php. The triceps data are available in the R package MultiKink (Wan and Zhong Citation2020), and can be accessed by typing data(triceps).","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"33 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933082","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}

{"title":"Penalised estimation of partially linear additive zero-inflated Bernoulli regression models","authors":"Minggen Lu, Chin-Shang Li, Karla D. Wagner","doi":"10.1080/10485252.2023.2275056","DOIUrl":"https://doi.org/10.1080/10485252.2023.2275056","url":null,"abstract":"AbstractWe develop a practical and computationally efficient penalised estimation approach for partially linear additive models to zero-inflated binary outcome data. To facilitate estimation, B-splines are employed to approximate unknown nonparametric components. A two-stage iterative expectation-maximisation (EM) algorithm is proposed to calculate penalised spline estimates. The large-sample properties such as the uniform convergence and the optimal rate of convergence for functional estimators, and the asymptotic normality and efficiency for regression coefficient estimators are established. Further, two variance-covariance estimation approaches are proposed to provide reliable Wald-type inference for regression coefficients. We conducted an extensive Monte Carlo study to evaluate the numerical properties of the proposed penalised methodology and compare it to the competing spline method [Li and Lu. ‘Semiparametric Zero-Inflated Bernoulli Regression with Applications’, Journal of Applied Statistics, 49, 2845–2869]. The methodology is further illustrated by an egocentric network study.Keywords: Additive Bernoulli regressionB-splineEM algorithmpenalised estimationzero-inflatedAMS SUBJECT CLASSIFICATIONS: 62G0562G2062G08 AcknowledgmentsThe authors are grateful to the Editor, the Associate Editor, and two reviewers for their useful comments and constructive suggestions which led to significant improvement in the revised manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was partially supported by the National Institute on Drug Abuse (NIDA) of the National Institutes of Health under Award Number R01DA038185.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"74 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235012","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}

{"title":"A modified Nadaraya–Watson procedure for variable selection and nonparametric prediction with missing data","authors":"Kin Yap Cheung, Stephen M. S. Lee","doi":"10.1080/10485252.2023.2270079","DOIUrl":"https://doi.org/10.1080/10485252.2023.2270079","url":null,"abstract":"AbstractWe propose a new method for variable selection and prediction under a nonparametric regression setting, where a covariate may be missing either because its value is hidden from the observer or because it is inapplicable to the particular subject being observed. Despite its practical relevance, the problem has received little attention in the literature and its solutions are largely non-existent. Our proposal hinges on the construction of a modified Nadaraya–Watson estimator of the conditional mean regression function, with its bandwidths regularised to select variables and its weights adapted to accommodate different types of missingness. The method allows for information sharing across different missing data patterns without affecting consistency of the estimator. Unlike other conventional methods such as those based on imputations or likelihoods, our method requires only mild assumptions on the model and the missingness mechanism. For prediction we focus on finding relevant variables for predicting mean responses, conditional on covariate vectors subject to a given type of missingness. Our theoretical and numerical results show that the new method is consistent in variable selection and yields better prediction accuracy compared to existing methods.KEYWORDS: Nadaraya–Watson estimatormissing datanonparametric regressionvariable selection Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883797","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}

{"title":"Threshold selection for extremal index estimation","authors":"Natalia M. Markovich, Igor V. Rodionov","doi":"10.1080/10485252.2023.2266050","DOIUrl":"https://doi.org/10.1080/10485252.2023.2266050","url":null,"abstract":"ABSTRACTWe propose a new threshold selection method for nonparametric estimation of the extremal index of stochastic processes. The discrepancy method was proposed as a data-driven smoothing tool for estimation of a probability density function. Now it is modified to select a threshold parameter of an extremal index estimator. A modification of the discrepancy statistic based on the Cramér–von Mises–Smirnov statistic ω2 is calculated by k largest order statistics instead of an entire sample. Its asymptotic distribution as k→∞ is proved to coincide with the ω2-distribution. Its quantiles are used as discrepancy values. The convergence rate of an extremal index estimate coupled with the discrepancy method is derived. The discrepancy method is used as an automatic threshold selection for the intervals and K-gaps estimators. It may be applied to other estimators of the extremal index. The performance of our method is evaluated by simulated and real data examples.KEYWORDS: Cramér–von Mises–Smirnov statisticdiscrepancy methodextremal indexnonparametric estimationthreshold selectionAMS SUBJECT CLASSIFICATION:: 62G32 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 The connection between (Equation1(1) ωn2=n∫−∞∞(Fn(x)−F(x))2dF(x)(1) ) and (Equation2(2) ω^n2(h)=∑i=1n(F^h(Xi,n)−i−0.5n)2+112n(2) ) can be found in Markovich (Citation2007, p. 81).2 Theoretically, events {Ti=1} are allowed. In practice, such cases related to single inter-arrival times between consecutive exceedances are meaningless.3 The modification (ω^n2−0.4/n+0.6/n2)(1+1/n) of classical statistic (Equation2(2) ω^n2(h)=∑i=1n(F^h(Xi,n)−i−0.5n)2+112n(2) ) eliminates the dependence of the percentage points of the C–M–S statistic on the sample size (Stephens Citation1974). For n>40 it changes the statistic on less than one percent. One can use the modification with regard to ω~L2(θ^) for finite L due to the closeness of its distribution to the limit distribution of the C–M–S statistic by Theorem 3.2.Additional informationFundingThe work of N.M. Markovich in Sections 1, 2, 4 and 5 was supported by the Russian Science Foundation [grant number 22-21-00177]. The work of I. V. Rodionov in Section 3 and proofs in Markovich and Rodionov (Citation2022) was performed at the Institute for Information Transmission Problems (Kharkevich Institute) of the Russian Academy of Sciences with the support of the Russian Science Foundation (grant No. 21-71-00035).","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135805169","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}