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Relative Error Prediction via Penalized Regression
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 Title & Authors
Relative Error Prediction via Penalized Regression
Jeong, Seok-Oh; Lee, Seo-Eun; Shin, Key-Il;
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 Abstract
This paper presents a new prediction method based on relative error incorporated with a penalized regression. The proposed method consists of fully data-driven procedures that is fast, simple, and easy to implement. An example of real data analysis and some simulation results were given to prove that the proposed approach works in practice.
 Keywords
prediction;relative error;penalized regression;LASSO;
 Language
Korean
 Cited by
 References
1.
Buelmann, P. and van de Geer, S. (2011). Statistics for High-Dimensional Data - Methods, Theory and Applications, Springer.

2.
Hwang, H.-J. and Shin, K.-I. (2008). Shrinkage prediction for small area estimation, The Korean Journal of Applied Statistics, 21, 109-123. crossref(new window)

3.
Jeong, S.-O. and Shin, K.-I. (2008). A new nonparametric method for prediction based on mean squared relative errors, Korean Communications in Statistics, 15, 255-264. crossref(new window)

4.
Jones, M. C., Park, H., Shin, K.-I., Vines, S. K. and Jeong, S.-O. (2008). Relative error prediction via kernel regression smoothers, Journal of Statistical Planning and Inference, 138, 2887-2898. crossref(new window)

5.
Park, H. and Shin, K.-I. (2006). A shrinked forecast in stationary process favoring percentage error, Journal of Time Series, 27, 129-139. crossref(new window)

6.
Park, H. and Stefanski, L. A. (1998). Relative-error prediction, Statistics and Probability Letters, 40, 227-236. crossref(new window)

7.
Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society B, 21, 279-289.