Title:
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Rank theory approach to ridge, LASSO, preliminary test and Stein-type estimators: Comparative study (English) |
Author:
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Saleh, A. K. Md. Ehsanes |
Author:
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Navrátil, Radim |
Language:
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English |
Journal:
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Kybernetika |
ISSN:
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0023-5954 (print) |
ISSN:
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1805-949X (online) |
Volume:
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54 |
Issue:
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5 |
Year:
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2018 |
Pages:
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958-977 |
Summary lang:
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English |
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Category:
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math |
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Summary:
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In the development of efficient predictive models, the key is to identify suitable predictors for a given linear model. For the first time, this paper provides a comparative study of ridge regression, LASSO, preliminary test and Stein-type estimators based on the theory of rank statistics. Under the orthonormal design matrix of a given linear model, we find that the rank based ridge estimator outperforms the usual rank estimator, restricted R-estimator, rank-based LASSO, preliminary test and Stein-type R-estimators uniformly. On the other hand, neither LASSO nor the usual R-estimator, preliminary test and Stein-type R-estimators outperform the other. The region of domination of LASSO over all the R-estimators (except the ridge R-estimator) is the interval around the origin of the parameter space. Finally, we observe that the L$_2$-risk of the restricted R-estimator equals the lower bound on the L$_2$-risk of LASSO. Our conclusions are based on L$_2$-risk analysis and relative L$_2$-risk efficiencies with related tables and graphs. (English) |
Keyword:
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efficiency of LASSO |
Keyword:
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penalty estimators |
Keyword:
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preliminary test |
Keyword:
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Stein-type estimator |
Keyword:
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ridge estimator |
Keyword:
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L$_2$-risk function |
MSC:
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62G05 |
MSC:
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62J05 |
MSC:
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62J07 |
idZBL:
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Zbl 07031754 |
idMR:
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MR3893130 |
DOI:
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10.14736/kyb-2018-5-0958 |
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Date available:
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2018-12-14T08:09:26Z |
Last updated:
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2020-01-05 |
Stable URL:
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http://hdl.handle.net/10338.dmlcz/147537 |
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Reference:
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