| bibtype |
J -
Journal Article
|
| ARLID |
0619694 |
| utime |
20250603102039.9 |
| mtime |
20250515235959.9 |
| SCOPUS |
105005102546 |
| WOS |
001488394600001 |
| DOI |
10.1007/s13171-025-00389-7 |
| title
(primary) (eng) |
A Class of Signed Rank Estimators in Regression Models with Random Covariates |
| specification |
| page_count |
21 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0619693 |
| ISSN |
0976-836X |
| title
|
Sankhy_A : The Indian Journal of Statistics |
|
| keyword |
Errors in variables |
| keyword |
Asymptotic relative efficiency |
| author
(primary) |
| ARLID |
cav_un_auth*0368969 |
| name1 |
Jurečková |
| name2 |
Jana |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| country |
CZ |
| share |
50% |
| garant |
S |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0488462 |
| name1 |
Koul |
| name2 |
H. L. |
| country |
US |
|
| source |
|
| cas_special |
| project |
| project_id |
GA22-03636S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0435411 |
|
| abstract
(eng) |
This note proves the asymptotic uniform linearity of a weighted empirical process of residual signed ranks and a class of linear residual signed rank statistics with bounded scores in nonlinear parametric regression models when covariates are random and independent of the errors. This result is used to derive limiting distributions of a class of signed rank estimators of the underlying regression parameters in these models. The latter result is applied to the errors in variables linear regression model to show that these estimators are robust against large measurement error in the sense that the asymptotic relative efficiency of a class of signed rank estimators against the bias corrected least square estimator tends to infinity as the measurement error variance tends \nto infinity (in some cases monotonically), when covariates and regression and measurement errors have Gaussian distributions. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2026 |
| num_of_auth |
2 |
| mrcbC55 |
BB |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0366806 |
| cooperation |
| ARLID |
cav_un_auth*0487717 |
| name |
Michigan State University, East Lansing, USA |
| institution |
MSU |
|
| confidential |
S |
| mrcbC91 |
A |
| mrcbT16-s |
0.321 |
| mrcbT16-E |
Q4 |
| arlyear |
2025 |
| mrcbU14 |
105005102546 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001488394600001 WOS |
| mrcbU63 |
cav_un_epca*0619693 Sankhy_A : The Indian Journal of Statistics 2025 0976-836X 0976-8378 |
|