Journal of Nonparametric Statistics最新文献

{"title":"Model-free prediction of time series: a nonparametric approach","authors":"Mohammad Mohammadi, Meng Li","doi":"10.1080/10485252.2023.2266740","DOIUrl":"https://doi.org/10.1080/10485252.2023.2266740","url":null,"abstract":"AbstractWe propose a novel approach for model-free time series forecasting. Unlike most existing methods, the proposed method does not rely on parametric error distributions nor assume parametric forms of the mean function, leading to broad applicability. We achieve such generality by establishing a simple but powerful representation of a time series {Xt;t∈Z} with suptE|Xt|<∞, that is, Xt has a solution which is a linear combination of infinite past values. Then using the obtained solution a prediction algorithm is presented, with large sample theoretical guarantees. Simulation studies show favourable performance of the proposed method compared with popular parametric and neural networks methods, and suggest its superiority when the sample size is small. An application to practical time series is discussed.Keywords: Predictionnonparametric methodsneural networksα-stable distributionMSC2010 subject classifications:: Primary: 60G25Secondary: 62M20 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 See https://www.sciencedirect.com/topics/engineering/left-inverse.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136097527","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":"On estimation of covariance function for functional data with detection limits","authors":"Haiyan Liu, Jeanine Houwing-Duistermaat","doi":"10.1080/10485252.2023.2258999","DOIUrl":"https://doi.org/10.1080/10485252.2023.2258999","url":null,"abstract":"In many studies on disease progression, biomarkers are restricted by detection limits, hence informatively missing. Current approaches ignore the problem by just filling in the value of the detection limit for the missing observations for the estimation of the mean and covariance function, which yield inaccurate estimation. Inspired by our recent work [Liu and Houwing-Duistermaat (2022), ‘Fast Estimators for the Mean Function for Functional Data with Detection Limits’, Stat, e467.] in which novel estimators for mean function for data subject to detection limit are proposed, in this paper, we will propose a novel estimator for the covariance function for sparse and dense data subject to a detection limit. We will derive the asymptotic properties of the estimator. We will compare our method to the standard method, which ignores the detection limit, via simulations. We will illustrate the new approach by analysing biomarker data subject to a detection limit. In contrast to the standard method, our method appeared to provide more accurate estimates of the covariance. Moreover its computation time is small.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135106403","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":"Fighting selection bias in statistical learning: application to visual recognition from biased image databases","authors":"Stephan Clémençon, Pierre Laforgue, Robin Vogel","doi":"10.1080/10485252.2023.2259011","DOIUrl":"https://doi.org/10.1080/10485252.2023.2259011","url":null,"abstract":"AbstractIn practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facial recognition systems with uneven performances on different population segments has highlighted the representativeness issues induced by a naive aggregation of the datasets. In this paper, we show how biasing models can remedy these problems. Based on the (approximate) knowledge of the biasing mechanisms at work, our approach consists in reweighting the observations, so as to form a nearly debiased estimator of the target distribution. One key condition is that the supports of the biased distributions must partly overlap, and cover the support of the target distribution. In order to meet this requirement in practice, we propose to use a low dimensional image representation, shared across the image databases. Finally, we provide numerical experiments highlighting the relevance of our approach.Keywords: Sampling biasselection effectvisual recognitionreliable statistical learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partially supported by the research chair ‘Good In Tech : Rethinking innovation and technology as drivers of a better world for and by humans’, under the auspices of the ‘Fondation du Risque’ and in partnership with the Institut Mines-Télécom, Sciences Po, Afnor, Ag2r La Mondiale, CGI France, Danone and Sycomore.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135106810","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":"Efficient nonparametric estimation of generalised autocovariances","authors":"Alessandra Luati, Francesca Papagni, Tommaso Proietti","doi":"10.1080/10485252.2023.2252527","DOIUrl":"https://doi.org/10.1080/10485252.2023.2252527","url":null,"abstract":"This paper provides a necessary and sufficient condition for asymptotic efficiency of a nonparametric estimator of the generalised autocovariance function of a stationary random process. The generalised autocovariance function is the inverse Fourier transform of a power transformation of the spectral density and encompasses the traditional and inverse autocovariance functions as particular cases. A nonparametric estimator is based on the inverse discrete Fourier transform of the power transformation of the pooled periodogram. We consider two cases: the fixed bandwidth design and the adaptive bandwidth design. The general result on the asymptotic efficiency, established for linear processes, is then applied to the class of stationary ARMA processes and its implications are discussed. Finally, we illustrate that for a class of contrast functionals and spectral densities, the minimum contrast estimator of the spectral density satisfies a Yule–Walker system of equations in the generalised autocovariance estimator.","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134950151","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":"Nonparametric relative error estimation of the regression function for left truncated and right censored time series data","authors":"N. Bayarassou, F. Hamrani, E. Ould Saïd","doi":"10.1080/10485252.2023.2241572","DOIUrl":"https://doi.org/10.1080/10485252.2023.2241572","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"30 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76728038","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":"Boundary-adaptive kernel density estimation: the case of (near) uniform density","authors":"J. Racine, Qi Li, Qiaoyu Wang","doi":"10.1080/10485252.2023.2250011","DOIUrl":"https://doi.org/10.1080/10485252.2023.2250011","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"110 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73284507","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":"Model checks for two-sample location-scale","authors":"Atefeh Javidialsaadi, Shoubhik Mondal, Sundarraman Subramanian","doi":"10.1080/10485252.2023.2243350","DOIUrl":"https://doi.org/10.1080/10485252.2023.2243350","url":null,"abstract":"MODEL CHECKS FOR TWO-SAMPLE LOCATION-SCALE by Atefeh Javidialsaadi Two-sample location-scale refers to a model that permits a pair of standardized random variables to have a common distribution. This means that if X1 and X2 are two random variables with means μ1 and μ2 and standard deviations σ1 and σ2, then (X1−μ1)/σ1 and (X2−μ2)/σ2 have some common unspecified standard or base distribution F0. Function-based hypothesis testing for these models refers to formal tests that would help determine whether or not two samples may have come from some location-scale family of distributions, without specifying the standard distribution F0. For uncensored data, Hall et al. (2013) proposed a test based on empirical characteristic functions (ECFs), but it can not be directly applied for censored data. Empirical likelihood with minimum distance (MD) plug-ins provides an alternative to the approach based on ECFs (Subramanian, 2020). However, when working with standardized data, it appeared feasible to set up plug-in empirical likelihood (PEL) with estimated means and standard deviations as plug-ins, which avoids MD estimation of location and scale parameters and (hence) quantile estimation. This project addresses two issues: (i) Set up a PEL founded testing procedure that uses sample means and standard deviations as the plug-ins for uncensored case, and Kaplan–Meier integral based estimators as plug-ins for censored case, (ii) Extend the ECF test to accommodate censoring. Large sample null distributions of the proposed test statistics are derived. Numerical studies are carried out to investigate the performance of the proposed methods. Real examples are also presented for both the uncensored and censored cases. MODEL CHECKS FOR TWO-SAMPLE LOCATION-SCALE by Atefeh Javidialsaadi A Dissertation Submitted to the Faculty of New Jersey Institute of Technology and Rutgers, The State University of New Jersey – Newark in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Mathematical Sciences Department of Mathematical Sciences Department of Mathematics and Computer Science, Rutgers-Newark","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"33 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80291186","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 varying coefficient model with matrix valued covariates","authors":"Hong-Fan Zhang","doi":"10.1080/10485252.2023.2238841","DOIUrl":"https://doi.org/10.1080/10485252.2023.2238841","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"5 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74329515","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":"Estimation and inference in functional varying-coefficient single-index quantile regression models","authors":"Hanbing Zhu, Tong Zhang, Yuanyuan Zhang, Heng Lian","doi":"10.1080/10485252.2023.2236722","DOIUrl":"https://doi.org/10.1080/10485252.2023.2236722","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82982829","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":"The scalar-on-function modal regression for functional time series data","authors":"Amel Azzi, Abderrahmane Belguerna, Ali Laksaci, Mustapha Rachdi","doi":"10.1080/10485252.2023.2233642","DOIUrl":"https://doi.org/10.1080/10485252.2023.2233642","url":null,"abstract":"","PeriodicalId":50112,"journal":{"name":"Journal of Nonparametric Statistics","volume":"34 4 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87668719","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}