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Original title:
Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /12./, Prague (CZ), 20180906
Year: 2018
Language: eng
Abstract: The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation coefficient based on the least weighted squares regression and the minimum weighted covariance determinant estimator, where the latter allows to estimate the mean and covariance matrix of multivariate data. New tools are proposed allowing to test hypotheses about these robust estimators or to estimate their variance. The techniques considered in the paper include resampling approaches with or without replacement, i.e. permutation tests, bootstrap variance estimation, and bootstrap confidence intervals. The performance of the newly described tools is illustrated on numerical examples. They reveal the suitability of the robust procedures also for non-contaminated data, as their confidence intervals are not much wider compared to those for standard maximum likelihood estimators. While resampling without replacement turns out to be more suitable for hypothesis testing, bootstrapping with replacement yields reliable confidence intervals but not corresponding hypothesis tests.
Keywords: correlation coefficient; econometrics; multivariate data; robust statistics
Project no.: GA17-07384S (CEP)
Funding provider: GA ČR
Host item entry: The 12th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-14-8
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2018/article/3-Kalina-Jan-paper.pdf
Original record: http://hdl.handle.net/11104/0289872
Permalink: http://www.nusl.cz/ntk/nusl-390097
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /12./, Prague (CZ), 20180906
Year: 2018
Language: eng
Abstract: The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation coefficient based on the least weighted squares regression and the minimum weighted covariance determinant estimator, where the latter allows to estimate the mean and covariance matrix of multivariate data. New tools are proposed allowing to test hypotheses about these robust estimators or to estimate their variance. The techniques considered in the paper include resampling approaches with or without replacement, i.e. permutation tests, bootstrap variance estimation, and bootstrap confidence intervals. The performance of the newly described tools is illustrated on numerical examples. They reveal the suitability of the robust procedures also for non-contaminated data, as their confidence intervals are not much wider compared to those for standard maximum likelihood estimators. While resampling without replacement turns out to be more suitable for hypothesis testing, bootstrapping with replacement yields reliable confidence intervals but not corresponding hypothesis tests.
Keywords: correlation coefficient; econometrics; multivariate data; robust statistics
Project no.: GA17-07384S (CEP)
Funding provider: GA ČR
Host item entry: The 12th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-14-8
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2018/article/3-Kalina-Jan-paper.pdf
Original record: http://hdl.handle.net/11104/0289872
Permalink: http://www.nusl.cz/ntk/nusl-390097
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2018-12-07, last modified 2023-12-06