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Využití bootstrapu a křížové validace v odhadu predikční chyby regresních modelů / Utilizing Bootstrap and Cross-validation for prediction error estimation in regression models

Finding a well-predicting model is one of the main goals of regression analysis. However, to evaluate a model's prediction abilities, it is a normal practice to use criteria which either do not serve this purpose, or criteria of insufficient reliability. As an alternative, there are relatively new methods which use repeated simulations for estimating an appropriate loss function -- prediction error. Cross-validation and bootstrap belong to this category. This thesis describes how to utilize these methods in order to select a regression model that best predicts new values of the response variable.

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:192850
Date January 2014
CreatorsLepša, Ondřej
ContributorsBašta, Milan, Malá, Ivana
PublisherVysoká škola ekonomická v Praze
Source SetsCzech ETDs
LanguageCzech
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

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