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Original title:
Least Weighted Absolute Value Estimator with an Application to Investment Data
Authors: Vidnerová, Petra ; Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /14./, Prague (CZ), 20200910
Year: 2020
Language: eng
Abstract: While linear regression represents the most fundamental model in current econometrics, the least squares (LS) estimator of its parameters is notoriously known to be vulnerable to the presence of outlying measurements (outliers) in the data. The class of M-estimators, thoroughly investigated since the groundbreaking work by Huber in 1960s, belongs to the classical robust estimation methodology (Jurečková et al., 2019). M-estimators are nevertheless not robust with respect to leverage points, which are defined as values outlying on the horizontal axis (i.e. outlying in one or more regressors). The least trimmed squares estimator seems therefore a more suitable highly robust method, i.e. with a high breakdown point (Rousseeuw & Leroy, 1987). Its version with weights implicitly assigned to individual observations, denoted as the least weighted squares estimator, was proposed and investigated in Víšek (2011). A trimmed estimator based on the 𝐿1-norm is available as the least trimmed absolute value estimator (Hawkins & Olive, 1999), which has not however acquired attention of practical econometricians. Moreover, to the best of our knowledge, its version with weights implicitly assigned to individual observations seems to be still lacking.
Keywords: computational aspects; implicit weighting; nonparametric bootstrap; regression median; robust regression
Project no.: GA18-23827S (CEP), GA19-05704S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: The 14th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-22-3
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2020/article/351-Vidnerova-Petra-paper.pdf
Original record: http://hdl.handle.net/11104/0313658
Permalink: http://www.nusl.cz/ntk/nusl-432444
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Vidnerová, Petra ; Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /14./, Prague (CZ), 20200910
Year: 2020
Language: eng
Abstract: While linear regression represents the most fundamental model in current econometrics, the least squares (LS) estimator of its parameters is notoriously known to be vulnerable to the presence of outlying measurements (outliers) in the data. The class of M-estimators, thoroughly investigated since the groundbreaking work by Huber in 1960s, belongs to the classical robust estimation methodology (Jurečková et al., 2019). M-estimators are nevertheless not robust with respect to leverage points, which are defined as values outlying on the horizontal axis (i.e. outlying in one or more regressors). The least trimmed squares estimator seems therefore a more suitable highly robust method, i.e. with a high breakdown point (Rousseeuw & Leroy, 1987). Its version with weights implicitly assigned to individual observations, denoted as the least weighted squares estimator, was proposed and investigated in Víšek (2011). A trimmed estimator based on the 𝐿1-norm is available as the least trimmed absolute value estimator (Hawkins & Olive, 1999), which has not however acquired attention of practical econometricians. Moreover, to the best of our knowledge, its version with weights implicitly assigned to individual observations seems to be still lacking.
Keywords: computational aspects; implicit weighting; nonparametric bootstrap; regression median; robust regression
Project no.: GA18-23827S (CEP), GA19-05704S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: The 14th International Days of Statistics and Economics Conference Proceedings, ISBN 978-80-87990-22-3
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://msed.vse.cz/msed_2020/article/351-Vidnerova-Petra-paper.pdf
Original record: http://hdl.handle.net/11104/0313658
Permalink: http://www.nusl.cz/ntk/nusl-432444
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2021-02-24, last modified 2023-12-06