| bibtype |
J -
Journal Article
|
| ARLID |
0520199 |
| utime |
20240903202657.1 |
| mtime |
20200116235959.9 |
| WOS |
000503976200004 |
| DOI |
10.2478/jamsi-2019-0008 |
| title
(primary) (eng) |
Statistical learning for recommending (robust) nonlinear regression methods |
| specification |
| page_count |
13 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0342185 |
| ISSN |
1336-9180 |
| title
|
Journal of applied mathematics, statistics and informatics |
| volume_id |
15 |
| volume |
2 (2019) |
| page_num |
47-59 |
| publisher |
| name |
Univerzita sv. Cyrila a Metoda v Trnave |
|
|
| keyword |
nonlinear least weighted squares |
| keyword |
optimal method selection |
| keyword |
optimization |
| keyword |
computations |
| author
(primary) |
| ARLID |
cav_un_auth*0345793 |
| name1 |
Kalina |
| name2 |
Jan |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0387590 |
| name1 |
Tichavský |
| name2 |
J. |
| country |
CZ |
|
| source |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0345381 |
| project_id |
GA17-07384S |
| agency |
GA ČR |
|
| project |
| ARLID |
cav_un_auth*0375756 |
| project_id |
GA19-05704S |
| agency |
GA ČR |
| country |
CZ |
|
| abstract
(eng) |
We are interested in comparing the performance of various nonlinear estimators of parameters of the standard nonlinear regression model. While the standard nonlinear least squares estimator is vulnerable to the presence of outlying measurements in the data, there exist several robust alternatives. However, it is not clear which estimator should be used for a given dataset and this question remains extremely difficult (or perhaps infeasible) to be answered theoretically. Metalearning represents a computationally intensive methodology for optimal selection of algorithms (or methods) and is used here to predict the most suitable nonlinear estimator for a particular dataset. The classification rule is learned over a training database of 24 publicly available datasets. The results of the primary learning give an interesting argument in favor of the nonlinear least weighted squares estimator, which turns out to be the most suitable one for the majority of datasets. The subsequent metalearning reveals that tests of normality and heteroscedasticity play a crucial role in finding the most suitable nonlinear estimator.\n |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2020 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0304884 |
| confidential |
S |
| mrcbC86 |
3+4 Article Mathematics Applied |
| mrcbC86 |
3+4 Article Mathematics Applied |
| mrcbC86 |
3+4 Article Mathematics Applied |
| mrcbC91 |
A |
| arlyear |
2019 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000503976200004 WOS |
| mrcbU63 |
cav_un_epca*0342185 Journal of applied mathematics, statistics and informatics 1336-9180 1339-0015 Roč. 15 č. 2 2019 47 59 Univerzita sv. Cyrila a Metoda v Trnave |
|