| bibtype |
A -
Abstract
|
| ARLID |
0491783 |
| utime |
20240103220258.3 |
| mtime |
20180726235959.9 |
| title
(primary) (eng) |
A comparison of robust nonlinear regression methods by statistical learning |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0491782 |
| ISBN |
978-88-61970-00-7 |
| title
|
ISNPS 2018. Book of Abstracts |
| page_num |
42-42 |
| publisher |
|
| editor |
|
| editor |
|
| editor |
| name1 |
Parella |
| name2 |
M. L. |
|
| editor |
|
| editor |
|
|
| keyword |
metalearning |
| keyword |
robust estimation |
| keyword |
nonlinear regression |
| keyword |
nonlinear regression quantiles |
| keyword |
heteroscedasticity |
| author
(primary) |
| ARLID |
cav_un_auth*0345793 |
| name1 |
Kalina |
| name2 |
Jan |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| institution |
UTIA-B |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0333704 |
| name1 |
Peštová |
| name2 |
Barbora |
| full_dept (cz) |
Oddělení statistického modelování |
| full_dept |
Department of Statistical Modelling |
| institution |
UIVT-O |
| full_dept |
Department of Statistical Modelling |
| fullinstit |
Ústav informatiky AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| abstract
(eng) |
Various estimators for the standard nonlinear regression model are compared with a focus on methods which are robust to outlying measurements in the data. The main contribution is a metalearning study which has the aim to predict the most suitable estimator for a particular data set. Here, various versions of the nonlinear least weighted squares estimator are compared with nonlinear least squares, nonlinear least trimmed squares and a nonlinear regression median, where the last is a special case of nonlinear regression quantiles. The metalearning study is performed over a data base of 24 economic data sets. The nonlinear least weighted squares estimator is able to yield the best result for the most data sets. The metalearning study gives advice how to select appropriate weights for the nonlinear least weighted squares, particularly it reveals tests of normality and heteroscedasticity to play a crucial role in finding suitable weights. |
| action |
| ARLID |
cav_un_auth*0362682 |
| name |
ISNPS 2018. Conference of the International Society for Nonparametric Statistics /4./ |
| dates |
20180611 |
| place |
Salerno |
| country |
IT |
| mrcbC20-s |
20180615 |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2019 |
| mrcbC52 |
4 O 4o 20231122143314.7 |
| inst_support |
RVO:67985807 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0285411 |
| confidential |
S |
| arlyear |
2018 |
| mrcbTft |
\nSoubory v repozitáři: a0491783.pdf |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0491782 ISNPS 2018. Book of Abstracts 2018 Salerno 42 42 978-88-61970-00-7 |
| mrcbU67 |
340 La Rocca M. |
| mrcbU67 |
340 Liseo B. |
| mrcbU67 |
340 Parella M. L. |
| mrcbU67 |
340 Salmaso L. |
| mrcbU67 |
340 Tardella L. |
|