<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is proposed. Unlike the classical bootstrap, it allows enriching a resampling scheme with values from outside the initial sample. Although a secondary sample may contain results beyond members of the primary set, they are generated smartly so that the crucial characteristics of the original observations remain invariant. Two methods for generating bootstrap samples preserving the representation (i.e., the value and the ambiguity or the expected value and the width) of fuzzy numbers belonging to the primary sample are suggested and numerically examined with respect to other approaches and various statistical properties.</p></abstract>

In this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is proposed. Unlike the classical bootstrap, it allows enriching a resampling scheme with values from outside the initial sample. Although a secondary sample may contain results beyond members of the primary set, they are generated smartly so that the crucial characteristics of the original observations remain invariant. Two methods for generating bootstrap samples preserving the representation (i.e., the value and the ambiguity or the expected value and the width) of fuzzy numbers belonging to the primary sample are suggested and numerically examined with respect to other approaches and various statistical properties.

Flexible resampling for fuzzy data

Faculty of Mathematics and Information Science

International Journal of Applied Mathematics and Computer Science

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

{"article-title":"Flexible resampling for fuzzy data"}

In this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is proposed. Unlike the classical bootstrap, it allows enriching a...

Flexible resampling for fuzzy data

Publié en ligne: 04 juil. 2020

Pages: 281 - 297

Reçu: 16 nov. 2019

Accepté: 29 avr. 2020

DOI: https://doi.org/10.34768/amcs-2020-0022

Mots clés
bootstrap, fuzzy data, fuzzy numbers, fuzzy sample, imprecise data, resampling

© 2020 Przemyslaw Grzegorzewski et al., published by Sciendo

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

Flexible resampling for fuzzy data

Publié en ligne: 04 juil. 2020

Pages: 281 - 297

Reçu: 16 nov. 2019

Accepté: 29 avr. 2020

DOI: https://doi.org/10.34768/amcs-2020-0022

Mots clésbootstrap, fuzzy data, fuzzy numbers, fuzzy sample, imprecise data, resampling

© 2020 Przemyslaw Grzegorzewski et al., published by Sciendo

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

Mots clés
bootstrap, fuzzy data, fuzzy numbers, fuzzy sample, imprecise data, resampling