A Monte Carlo comparison of regression estimators when the error distribution is long-tailed symmetric
Journal of Modern Applied Statistical Methods, ISSN: 1538-9472, Vol: 8, Issue: 1, Page: 161-172
2009
- 3Citations
- 536Usage
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations3
- Citation Indexes3
- CrossRef3
- Usage536
- Downloads459
- Abstract Views77
- Captures1
- Readers1
Article Description
The performances of the ordinary least squares (OLS), modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares (WIN), trimmed least squares (TLS), Theil's (Theil) and weighted Theil's (Weighted Theil) estimators are compared under the simple linear regression model in terms of their bias and efficiency when the distribution of error terms is long-tailed symmetric. © 2009 JMASM, Inc.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=78650775835&origin=inward; http://dx.doi.org/10.22237/jmasm/1241136780; https://jmasm.com/index.php/jmasm/article/view/408; https://digitalcommons.wayne.edu/jmasm/vol8/iss1/14; https://digitalcommons.wayne.edu/cgi/viewcontent.cgi?article=1490&context=jmasm; https://dx.doi.org/10.22237/jmasm/1241136780
The Netherlands Press
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