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Least squares estimation for binary decision trees

In this thesis, a binary decision tree is used as an approximation of a nonparametric regression curve. The best fitted decision tree is estimated from data via least squares method. It is investigated how and under which conditions the estimator converges.
These asymptotic results then are used to create asymptotic convergence regions.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:73135
Date14 December 2020
CreatorsAlbrecht, Nadine
ContributorsFerger, Dietmar, Stute, Winfried, Technische Universität Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess

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