{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1361137043755048832.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1111/1467-9868.00283"}},{"identifier":{"@type":"URI","@value":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1467-9868.00283"}},{"identifier":{"@type":"URI","@value":"https://academic.oup.com/jrsssb/article-pdf/63/2/243/49590422/jrsssb_63_2_243.pdf"}}],"dc:title":[{"@value":"Nonparametric Maximum Likelihood Estimation for Shifted Curves"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:title>Summary</jats:title>\n               <jats:p>The analysis of a sample of curves can be done by self-modelling regression methods. Within this framework we follow the ideas of nonparametric maximum likelihood estimation known from event history analysis and the counting process set-up. We derive an infinite dimensional score equation and from there we suggest an algorithm to estimate the shape function for a simple shape invariant model. The nonparametric maximum likelihood estimator that we find turns out to be a Nadaraya–Watson-like estimator, but unlike in the usual kernel smoothing situation we do not need to select a bandwidth or even a kernel function, since the score equation automatically selects the shape and the smoothing parameter for the estimation. We apply the method to a sample of electrophoretic spectra to illustrate how it works.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380298342674497536","@type":"Researcher","foaf:name":[{"@value":"Birgitte B. Rønn"}],"jpcoar:affiliationName":[{"@value":"Royal Veterinary and Agricultural University , Copenhagen , Denmark"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"13697412"},{"@type":"EISSN","@value":"14679868"}],"prism:publicationName":[{"@value":"Journal of the Royal Statistical Society Series B: Statistical Methodology"}],"dc:publisher":[{"@value":"Oxford University Press (OUP)"}],"prism:publicationDate":"2001-07-01","prism:volume":"63","prism:number":"2","prism:startingPage":"243","prism:endingPage":"259"},"reviewed":"false","dc:rights":["https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model"],"url":[{"@id":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1467-9868.00283"},{"@id":"https://academic.oup.com/jrsssb/article-pdf/63/2/243/49590422/jrsssb_63_2_243.pdf"}],"createdAt":"2003-03-12","modifiedAt":"2023-03-22","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360004231602193024","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Robust curve registration using the t distribution"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1111/1467-9868.00283"},{"@type":"CROSSREF","@value":"10.1007/s41237-019-00077-5_references_DOI_XY5Hufs0PO6VNABWa4f085fwyXg"}]}