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Ocenjevanje standardne napake in intervalov zaupanja z metodo bootstrap
GAŠPARAC, GRETA (Author), Štrumbelj, Erik (Mentor) More about this mentor... This link opens in a new window

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Abstract
V diplomskem delu predstavimo metodo bootstrap, ki jo uvrščamo v družino metod samovzorčenja ter je preprostejša in bolj intuitivna alternativa tradicionalnim statističnim metodam za ocenjevanje negotovosti. Osredotočimo se na neparametrično različico metode. Opišemo njene lastnosti in jih predstavimo s praktičnimi primeri s področja strojnega učenja - ocenjevanje in primerjava različnih modelov. Izpostavimo tudi šibke točke metode. Predstavimo in primerjamo tri intervale zaupanja bootstrap: standardnega normalnega z uporabo standardne napake bootstrap in dva klasična intervala bootstrap, centilnega in BCa. Pričakovano se v večini primerov najbolje obnese BCa.

Language:Slovenian
Keywords:metoda bootstrap, standardna napaka, intervali zaupanja
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2018
Views:228
Downloads:163
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Secondary language

Language:English
Title:Boostraping standard errors and confidence intervals
Abstract:
We introduce the reader to the bootstrap, a simple and flexible resampling-based alternative for quantifying uncertainty. We describe the basic characteristics of the non-parametric bootstrap and illustrate its practical behaviour with simulations in the context of a typical task in machine learning - estimating and comparing the performance of different prediction models. We also present some of the method's weaknesses. We introduce and compare three standard intervals: the standard normal using bootstrap standard error and two more typical bootstrap confidence intervals, the percentile and the BCa interval. As theory suggests, the BCa performs the best over a wide range of situations.

Keywords:bootstrap, standard error, confidence intervals

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