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Boostraping standard errors and confidence intervals

Greta Gašparac (2018) Boostraping standard errors and confidence intervals. EngD thesis.

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    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.

    Item Type: Thesis (EngD thesis)
    Keywords: bootstrap, standard error, confidence intervals
    Number of Pages: 43
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Erik Štrumbelj5570Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537935299)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 4214
    Date Deposited: 13 Sep 2018 17:04
    Last Modified: 28 Sep 2018 12:43
    URI: http://eprints.fri.uni-lj.si/id/eprint/4214

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