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Journal of Nonparametric Statistics最新文献

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Avoiding the Surrogate Paradox: An Empirical Framework for Assessing Assumptions. 避免代理悖论：评估假设的经验框架。
IF 0.9 4区 数学
Journal of Nonparametric Statistics Pub Date : 2025-05-12 DOI: 10.1080/10485252.2025.2498609
Emily Hsiao, Lu Tian, Layla Parast
{"title":"Avoiding the Surrogate Paradox: An Empirical Framework for Assessing Assumptions.","authors":"Emily Hsiao, Lu Tian, Layla Parast","doi":"10.1080/10485252.2025.2498609","DOIUrl":"https://doi.org/10.1080/10485252.2025.2498609","url":null,"abstract":"<p><p>The use of surrogate markers to replace a primary outcome in clinical trials has the potential to allow earlier decisions about the effectiveness of a treatment when a direct measurement of the primary outcome is difficult to obtain. However, the surrogate paradox, which occurs when a treatment has a positive effect on the surrogate marker but a negative effect on the primary outcome, may lead researchers to make incorrect conclusions about the treatment benefit. In this paper, we propose a formal nonparametric framework to empirically examine and test assumptions that ensure avoidance of the surrogate paradox. For each assumption, we propose a nonparametric hypothesis test, formally derive the properties of the test, and analyze its performance in finite samples in a variety of simulation settings. We apply our proposed testing framework to data from the the Diabetes Prevention Program clinical trial.</p>","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12382359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Nonparametric Density Estimation for Data Scattered on Irregular Spatial Domains: A Likelihood-Based Approach Using Bivariate Penalized Spline Smoothing. 不规则空间域离散数据的非参数密度估计：一种基于似然的二元惩罚样条平滑方法。
IF 0.9 4区 数学
Journal of Nonparametric Statistics Pub Date : 2025-04-28 DOI: 10.1080/10485252.2025.2497541
Kunal Das, Shan Yu, Guannan Wang, Li Wang
{"title":"Nonparametric Density Estimation for Data Scattered on Irregular Spatial Domains: A Likelihood-Based Approach Using Bivariate Penalized Spline Smoothing.","authors":"Kunal Das, Shan Yu, Guannan Wang, Li Wang","doi":"10.1080/10485252.2025.2497541","DOIUrl":"10.1080/10485252.2025.2497541","url":null,"abstract":"<p><p>Accurately estimating data density is crucial for making informed decisions and modeling in various fields. This paper presents a novel nonparametric density estimation procedure that utilizes bivariate penalized spline smoothing over triangulation for data scattered over irregular spatial domains. Our likelihood-based approach incorporates a regularization term addressing the roughness of the logarithm of density using a second-order differential operator. We establish the asymptotic convergence rate of the proposed density estimator in terms of the <math> <msub><mrow><mi>L</mi></mrow> <mrow><mn>2</mn></mrow> </msub> </math> and <math> <msub><mrow><mi>L</mi></mrow> <mrow><mo>∞</mo></mrow> </msub> </math> norms under mild natural conditions, providing a solid theoretical foundation. The proposed method demonstrates superior efficiency and flexibility with enhanced smoothness and continuity across the domain compared to existing techniques. We validate our approach through comprehensive simulation studies and apply it to real-world motor vehicle theft data from Portland, Oregon, illustrating its practical advantages in data analysis on spatial domains.</p>","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates. 稀疏纵向协变量时变系数乘性风险模型的回归分析。
IF 0.9 4区 数学
Journal of Nonparametric Statistics Pub Date : 2025-02-19 DOI: 10.1080/10485252.2025.2466649
Zhuowei Sun, Hongyuan Cao
{"title":"Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates.","authors":"Zhuowei Sun, Hongyuan Cao","doi":"10.1080/10485252.2025.2466649","DOIUrl":"10.1080/10485252.2025.2466649","url":null,"abstract":"<p><p>We study the multiplicative hazards model with intermittently observed longitudinal covariates and time-varying coefficients. For such models, the existing <i>ad hoc</i> approach, such as the last value carried forward, is biased. We propose a kernel weighting approach to get an unbiased estimation of the non-parametric coefficient function and establish asymptotic normality for any fixed time point. Furthermore, we construct the simultaneous confidence band to examine the overall magnitude of the variation. Simulation studies support our theoretical predictions and show favorable performance of the proposed method. A data set from Alzheimer's Disease Neuroimaging Initiative study is used to illustrate our methodology.</p>","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145233977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-Based Data. 一种新的基于曲面数据的三角球面样条平滑方法。
IF 0.9 4区 数学
Journal of Nonparametric Statistics Pub Date : 2025-01-01 Epub Date: 2025-01-07 DOI: 10.1080/10485252.2025.2449886
Zhiling Gu, Shan Yu, Guannan Wang, Ming-Jun Lai, Lily Wang
{"title":"TSSS: A Novel Triangulated Spherical Spline Smoothing for Surface-Based Data.","authors":"Zhiling Gu, Shan Yu, Guannan Wang, Ming-Jun Lai, Lily Wang","doi":"10.1080/10485252.2025.2449886","DOIUrl":"10.1080/10485252.2025.2449886","url":null,"abstract":"<p><p>Surface-based data are prevalent across diverse practical applications in various fields. This paper introduces a novel nonparametric method to discover the underlying signals from data distributed on complex surface-based domains. The proposed approach involves a penalized spline estimator defined on a triangulation of surface patches, enabling effective signal extraction and recovery. The proposed method offers superior handling of \"leakage\" or \"boundary effects\" over complex domains, enhanced computational efficiency, and capabilities for analyzing sparse and irregularly distributed data on complex objects. We provide rigorous theoretical guarantees, including convergence rates and asymptotic normality of the estimators. We demonstrate that the convergence rates are optimal within the framework of nonparametric estimation. A bootstrap method is introduced to quantify the uncertainty in the proposed estimators and to provide pointwise confidence intervals. The advantages of the proposed method are demonstrated through simulations and data applications on cortical surface neuroimaging data and oceanic near-surface atmospheric data.</p>","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"37 3","pages":"683-712"},"PeriodicalIF":0.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12419768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Adaptive and efficient isotonic estimation in Wicksell's problem 维克塞尔问题中的自适应高效等差数列估计
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2024-09-10 DOI: 10.1080/10485252.2024.2397680
Francesco Gili, Geurt Jongbloed, Aad van der Vaart
{"title":"Adaptive and efficient isotonic estimation in Wicksell's problem","authors":"Francesco Gili, Geurt Jongbloed, Aad van der Vaart","doi":"10.1080/10485252.2024.2397680","DOIUrl":"https://doi.org/10.1080/10485252.2024.2397680","url":null,"abstract":"We consider nonparametric estimation in Wicksell's problem, which has applications in astronomy for estimating the distribution of star positions in a galaxy and in material sciences for determinin...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"30 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227340","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
A general semi-parametric elliptical distribution model for semi-supervised learning 用于半监督学习的通用半参数椭圆分布模型
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2024-08-20 DOI: 10.1080/10485252.2024.2393725
Chin-Tsang Chiang, Sheng-Hsin Fan, Ming-Yueh Huang, Jen-Chieh Teng, Alvin Lim
{"title":"A general semi-parametric elliptical distribution model for semi-supervised learning","authors":"Chin-Tsang Chiang, Sheng-Hsin Fan, Ming-Yueh Huang, Jen-Chieh Teng, Alvin Lim","doi":"10.1080/10485252.2024.2393725","DOIUrl":"https://doi.org/10.1080/10485252.2024.2393725","url":null,"abstract":"This research proposes a novel semi-parametric elliptical distribution model for application in semi-supervised learning tasks. We use labelled and unlabelled data to develop a pseudo maximum likel...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"6 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267841","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
Stone's theorem for distributional regression in Wasserstein distance 瓦瑟斯坦距离分布回归的斯通定理
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2024-08-17 DOI: 10.1080/10485252.2024.2393172
Clément Dombry, Thibault Modeste, Romain Pic
{"title":"Stone's theorem for distributional regression in Wasserstein distance","authors":"Clément Dombry, Thibault Modeste, Romain Pic","doi":"10.1080/10485252.2024.2393172","DOIUrl":"https://doi.org/10.1080/10485252.2024.2393172","url":null,"abstract":"We extend the celebrated Stone's theorem to the framework of distributional regression. More precisely, we prove that weighted empirical distributions with local probability weights satisfying the ...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"12 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227341","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
Kernel density estimation for a stochastic process with values in a Riemannian manifold 具有黎曼流形中数值的随机过程的核密度估计
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2024-08-13 DOI: 10.1080/10485252.2024.2382442
Mohamed Abdillahi Isman, Wiem Nefzi, Papa Mbaye, Salah Khardani, Anne-Françoise Yao
{"title":"Kernel density estimation for a stochastic process with values in a Riemannian manifold","authors":"Mohamed Abdillahi Isman, Wiem Nefzi, Papa Mbaye, Salah Khardani, Anne-Françoise Yao","doi":"10.1080/10485252.2024.2382442","DOIUrl":"https://doi.org/10.1080/10485252.2024.2382442","url":null,"abstract":"This paper is related to the issue of the density estimation of observations with values in a Riemannian submanifold. In this context, Henry and Rodriguez ((2009), ‘Kernel Density Estimation on Rie...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"3 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220145","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
Functional index coefficient models for locally stationary time series 局部静止时间序列的函数指数系数模型
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2024-08-05 DOI: 10.1080/10485252.2024.2387781
Xin Guan, Qunfang Xu, Jinhong You, Yong Zhou
{"title":"Functional index coefficient models for locally stationary time series","authors":"Xin Guan, Qunfang Xu, Jinhong You, Yong Zhou","doi":"10.1080/10485252.2024.2387781","DOIUrl":"https://doi.org/10.1080/10485252.2024.2387781","url":null,"abstract":"In the analysis of nonlinear time series, we propose a novel functional index coefficient model for the locally stationary data. The proposed model can effectively capture the dynamic interaction e...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"59 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947922","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
Generalized fiducial inference for the GEV change-point model GEV 变化点模型的广义基准推理
IF 1.2 4区 数学
Journal of Nonparametric Statistics Pub Date : 2024-08-04 DOI: 10.1080/10485252.2024.2387091
Xia Cai, Yaru Qiao, Jiahua Qiao, Liang Yan
{"title":"Generalized fiducial inference for the GEV change-point model","authors":"Xia Cai, Yaru Qiao, Jiahua Qiao, Liang Yan","doi":"10.1080/10485252.2024.2387091","DOIUrl":"https://doi.org/10.1080/10485252.2024.2387091","url":null,"abstract":"Generalized extreme value (GEV) distribution is used to analyse the maximum from a block of data. It is very useful to describe the unusual event rather than the usual event. In this paper, we prop...","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"28 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947920","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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