- Path:
Periodical
- Title:
- Stochastics and quality control
- Publication:
-
Berlin: De Gruyter
- Note:
- Gesehen am 11.09.17
- 355!URL-Ä(29-06-22)
- Scope:
- Online-Ressource
- ISSN:
- 2367-2404
- ZDB-ID:
-
2905267-1
- VÖBB-Katalog:
- 35425625
- Previous Title:
- Economic quality control
- Keywords:
- Zeitschrift
- Classification:
- Technik
- Collection:
- Technik
- Copyright:
- Rights reserved
- Accessibility:
- Eingeschränkter Zugang mit Nutzungsbeschränkungen
- Title:
- Stochastics and quality control
- Publication:
-
Berlin: De Gruyter
- Note:
- Gesehen am 11.09.17
- 355!URL-Ä(29-06-22)
- Scope:
- Online-Ressource
- ISSN:
- 2367-2404
- ZDB-ID:
-
2905267-1
- VÖBB-Katalog:
- 35425625
- Previous Title:
- Economic quality control
- Keywords:
- Zeitschrift
- Classification:
- Technik
- Collection:
- Technik
- Copyright:
- Rights reserved
- Accessibility:
- Eingeschränkter Zugang mit Nutzungsbeschränkungen
Article
- Title:
- Comparing Ridge Regression Estimators: Exploring Both New and Old Methods
- Publication:
-
Berlin: De Gruyter, 2025
- Language:
- English
- Information:
- Abstract: Ridge regression presents a method to tackle multicollinearity issues. Several estimators and predictors for the estimation of biasing parameter k have been extensively detailed in scholarly literature. We offer a thorough analysis of both conventional and emerging methods aimed at precisely determining the ridge parameter k. Our investigation provides valuable insights into the properties of these estimators and their practical efficacy in various applications. Proposed estimators for the parameter k are assessed using Monte Carlo simulations and a real-world example, with a focus on evaluating their performance based on Mean Squared Error (MSE). Our estimator, in conjunction with others, showcases commendable performance, as indicated by the results.
- Scope:
- Online-Ressource
- Note:
- Kein Open Access
- Archivierung/Langzeitarchivierung gewährleistet
- Keywords:
- Linear Models ; MSE ; Multicollinearity ; Ridge Regression Simulation Study ; 62J07 ; 62J05 ; 97K80
- Copyright:
- Rights reserved
- Accessibility:
- Eingeschränkter Zugang mit Nutzungsbeschränkungen
- Title:
- Comparing Ridge Regression Estimators: Exploring Both New and Old Methods
- Publication:
-
Berlin: De Gruyter, 2025
- Language:
- English
- Information:
- Abstract: Ridge regression presents a method to tackle multicollinearity issues. Several estimators and predictors for the estimation of biasing parameter k have been extensively detailed in scholarly literature. We offer a thorough analysis of both conventional and emerging methods aimed at precisely determining the ridge parameter k. Our investigation provides valuable insights into the properties of these estimators and their practical efficacy in various applications. Proposed estimators for the parameter k are assessed using Monte Carlo simulations and a real-world example, with a focus on evaluating their performance based on Mean Squared Error (MSE). Our estimator, in conjunction with others, showcases commendable performance, as indicated by the results.
- Scope:
- Online-Ressource
- Note:
- Kein Open Access
- Archivierung/Langzeitarchivierung gewährleistet
- Keywords:
- Linear Models ; MSE ; Multicollinearity ; Ridge Regression Simulation Study ; 62J07 ; 62J05 ; 97K80
- Copyright:
- Rights reserved
- Accessibility:
- Eingeschränkter Zugang mit Nutzungsbeschränkungen
