Details
| Original language | English |
|---|---|
| Title of host publication | Prediction and Causality in Econometrics and Related Topics |
| Editors | Nguyen Ngoc Thach, Doan Thanh Ha, Nguyen Duc Trung, Vladik Kreinovich |
| Place of Publication | Cham |
| Pages | 123-130 |
| Number of pages | 8 |
| ISBN (electronic) | 978-3-030-77094-5 |
| Publication status | Published - 27 Jul 2021 |
| Event | Fourth International Econometric Conference of Vietnam - Ho Chi Minh City, Viet Nam Duration: 11 Jan 2021 → 13 Jan 2021 Conference number: 4 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 983 |
| ISSN (Print) | 1860-949X |
| ISSN (electronic) | 1860-9503 |
Abstract
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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Prediction and Causality in Econometrics and Related Topics. ed. / Nguyen Ngoc Thach; Doan Thanh Ha; Nguyen Duc Trung; Vladik Kreinovich. Cham, 2021. p. 123-130 (Studies in Computational Intelligence; Vol. 983).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Why LASSO, Ridge Regression, and EN
T2 - Fourth International Econometric Conference of Vietnam
AU - Yamaka, Woraphon
AU - Alkhatib, Hamza
AU - Neumann, Ingo
AU - Kreinovich, Vladik
N1 - Conference code: 4
PY - 2021/7/27
Y1 - 2021/7/27
N2 - In many practical situations, observations and measurement results are consistent with many different models–i.e., the corresponding problem is ill-posed. In such situations, a reasonable idea is to take into account that the values of the corresponding parameters should not be too large; this idea is known as regularization. Several different regularization techniques have been proposed; empirically the most successful are LASSO method, when we bound the sum of absolute values of the parameters, ridge regression method, when we bound the sum of the squares, and a EN method in which these two approaches are combined. In this paper, we explain the empirical success of these methods by showing that these methods can be naturally derived from soft computing ideas.
AB - In many practical situations, observations and measurement results are consistent with many different models–i.e., the corresponding problem is ill-posed. In such situations, a reasonable idea is to take into account that the values of the corresponding parameters should not be too large; this idea is known as regularization. Several different regularization techniques have been proposed; empirically the most successful are LASSO method, when we bound the sum of absolute values of the parameters, ridge regression method, when we bound the sum of the squares, and a EN method in which these two approaches are combined. In this paper, we explain the empirical success of these methods by showing that these methods can be naturally derived from soft computing ideas.
UR - http://www.scopus.com/inward/record.url?scp=85113375847&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-77094-5_12
DO - 10.1007/978-3-030-77094-5_12
M3 - Conference contribution
SN - 9783030770938
T3 - Studies in Computational Intelligence
SP - 123
EP - 130
BT - Prediction and Causality in Econometrics and Related Topics
A2 - Ngoc Thach, Nguyen
A2 - Ha, Doan Thanh
A2 - Trung, Nguyen Duc
A2 - Kreinovich, Vladik
CY - Cham
Y2 - 11 January 2021 through 13 January 2021
ER -