Pub Date : 2023-12-26DOI: 10.1080/10485252.2023.2296523
Rida Benhaddou, Qing Liu
{"title":"Wavelet estimation for the nonparametric additive model in random design and long-memory dependent errors","authors":"Rida Benhaddou, Qing Liu","doi":"10.1080/10485252.2023.2296523","DOIUrl":"https://doi.org/10.1080/10485252.2023.2296523","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"5 12","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139156464","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 : 2023-12-14DOI: 10.1080/10485252.2023.2292690
Weixuan Zhu, Tiantian Zuo, Chunlin Wang
Approximate Bayesian computation (ABC) is a likelihood-free inference method commonly employed for statistical inference in models with unknown or complex likelihood functions. ABC estimates the po...
{"title":"Approximate Bayesian computation with semiparametric density ratio model","authors":"Weixuan Zhu, Tiantian Zuo, Chunlin Wang","doi":"10.1080/10485252.2023.2292690","DOIUrl":"https://doi.org/10.1080/10485252.2023.2292690","url":null,"abstract":"Approximate Bayesian computation (ABC) is a likelihood-free inference method commonly employed for statistical inference in models with unknown or complex likelihood functions. ABC estimates the po...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"184 1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138685543","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 : 2023-12-11DOI: 10.1080/10485252.2023.2291430
Benedikt Funke, Masayuki Hirukawa
This paper studies the problem of estimating the first-order derivative of an unknown density with support on R+ or [0,1]. Nonparametric density derivative estimators smoothed by the asymmetric, ga...
研究了在R+或[0,1]支持下未知密度一阶导数的估计问题。非参数密度导数估计被非对称、ga…
{"title":"Density derivative estimation using asymmetric kernels","authors":"Benedikt Funke, Masayuki Hirukawa","doi":"10.1080/10485252.2023.2291430","DOIUrl":"https://doi.org/10.1080/10485252.2023.2291430","url":null,"abstract":"This paper studies the problem of estimating the first-order derivative of an unknown density with support on R+ or [0,1]. Nonparametric density derivative estimators smoothed by the asymmetric, ga...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"24 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138632524","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 : 2023-12-11DOI: 10.1080/10485252.2023.2292684
Li Cai, Yao Yao, Suojin Wang
A weighted local linear estimator for the mean function is constructed based on nonparametrically estimated selection probabilities when covariates are missing at random. The weighted estimator is ...
{"title":"Oracle-efficient estimation for the mean function of missing covariate data based on noparametrically estimated selection probabilities","authors":"Li Cai, Yao Yao, Suojin Wang","doi":"10.1080/10485252.2023.2292684","DOIUrl":"https://doi.org/10.1080/10485252.2023.2292684","url":null,"abstract":"A weighted local linear estimator for the mean function is constructed based on nonparametrically estimated selection probabilities when covariates are missing at random. The weighted estimator is ...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"101 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138631446","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 : 2023-12-05DOI: 10.1080/10485252.2023.2288842
B. L. S. Prakasa Rao
We investigate the asymptotic properties of a kernel-type nonparametric estimator of the linear multiplier in models governed by a stochastic differential equation driven by a general Gaussian process.
研究了由一般高斯过程驱动的随机微分方程控制的模型中线性乘子的核型非参数估计的渐近性质。
{"title":"Nonparametric estimation of linear multiplier in SDEs driven by general Gaussian processes","authors":"B. L. S. Prakasa Rao","doi":"10.1080/10485252.2023.2288842","DOIUrl":"https://doi.org/10.1080/10485252.2023.2288842","url":null,"abstract":"We investigate the asymptotic properties of a kernel-type nonparametric estimator of the linear multiplier in models governed by a stochastic differential equation driven by a general Gaussian process.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542731","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 : 2023-11-27DOI: 10.1080/10485252.2023.2284896
Carina Beering, Anne Leucht
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...
本文给出了一类具有时变系数的可能非因果多元线性过程的光滑函数的泛函中心极限定理。由于限制过程…
{"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}
Pub Date : 2023-11-27DOI: 10.1080/10485252.2023.2284897
John O'Quigley
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...
{"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}
Pub Date : 2023-11-10DOI: 10.1080/10485252.2023.2280003
Alejandro Cholaquidis, Ricardo Fraiman, Manuel Hernández-Banadik
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].
摘要利用GPS提供的轨迹信息，建立了新的连续时间模型和统计方法来估计与动物运动有关的一些集合，如起始距离和核心区域等。由于数据传输成本和GPS电池寿命是实际限制，实验设计者必须做出关键的采样决策，以最大限度地提高信息。我们介绍了开关采样方案，其中GPS交替打开和关闭。该方案已在实践中得到应用，但缺乏统计学理论支持。在这种抽样方法下，我们证明了具有底层反射扩散模型的家园距离估计的一致性。与GPS在整个实验中始终开着的情况下达到相同的收敛速率。仿真研究和实际数据说明了这一点。我们还提供了平稳分布、其水平集和漂移函数的估计。关键词:距离估计;反映布朗运动与漂移平稳分布;水平集估计;2010数学学科分类:62M2062G2060J70致谢我们感谢马克斯普朗克鸟类研究所的Stephen Blake博士，他为本文使用的数据集提供了方便。支持本研究结果的数据可以在Movebank上公开获取，网址为https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1818825，参考编号为1818825。我们感谢编辑和三位审稿人的建设性意见，这些意见大大改进了当前版本的手稿。披露声明作者未报告潜在的利益冲突。本研究由ANII基金(Agencia Nacional de Investigación e Innovación)支持[资助号POSNAC20191157608, FCE120191156054]。
{"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}
Pub Date : 2023-11-10DOI: 10.1080/10485252.2023.2280016
Mingyue Du, Mengzhu Yu
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).
{"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}
Pub Date : 2023-11-09DOI: 10.1080/10485252.2023.2280004
Xuejun Wang, Xi Chen, Tien-Chung Hu, Andrei Volodin
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).
{"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}
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：