<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The bootstrap method is a well-known method to gather a full probability distribution from the dataset of a small sample. The simple bootstrap i.e. resampling from the raw dataset often leads to a significant irregularities in a shape of resulting empirical distribution due to the discontinuity of a support. The remedy for these irregularities is the smoothed bootstrap: a small random shift of source points before each resampling. This shift is controlled by specifically selected distributions. The key issue is such parameter settings of these distributions to achieve the desired characteristics of the empirical distribution. This paper describes an example of this procedure.</p>
</abstract>

The bootstrap method is a well-known method to gather a full probability distribution from the dataset of a small sample. The simple bootstrap i.e. resampling from the raw dataset often leads to a significant irregularities in a shape of resulting empirical distribution due to the discontinuity of a support. The remedy for these irregularities is the smoothed bootstrap: a small random shift of source points before each resampling. This shift is controlled by specifically selected distributions. The key issue is such parameter settings of these distributions to achieve the desired characteristics of the empirical distribution. This paper describes an example of this procedure.

The Smoothed Bootstrap Fine-Tuning

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{"article-title":"The Smoothed Bootstrap Fine-Tuning"}

The bootstrap method is a well-known method to gather a full probability distribution from the dataset of a small sample. The simple bootstrap i.e. resampling...

The Smoothed Bootstrap Fine-Tuning

Published Online: Mar 28, 2019

Page range: 716 - 723

Received: Nov 24, 2018

Accepted: Jan 30, 2019

DOI: https://doi.org/10.2478/czoto-2019-0091

Keywords
smoothed bootstrap, statistics, design of experiments, numerical simulation

© 2019 Renata Dwornicka et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The Smoothed Bootstrap Fine-Tuning

Published Online: Mar 28, 2019

Page range: 716 - 723

Received: Nov 24, 2018

Accepted: Jan 30, 2019

DOI: https://doi.org/10.2478/czoto-2019-0091

Keywordssmoothed bootstrap, statistics, design of experiments, numerical simulation

© 2019 Renata Dwornicka et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
smoothed bootstrap, statistics, design of experiments, numerical simulation