Artikel
Regression based scenario generation: Applications for performance management
Regression analysis is a common tool in performance management and measurement in industry. Many firms wish to optimise their performance using Stochastic Programming but to the best of our knowledge there exists no scenario generation method for regression models. In this paper we propose a new scenario generation method for linear regression used in performance management. Our scenario generation method is able to produce more representative scenarios by utilising the data driven properties of linear regression models and cluster based resampling. Secondly, our scenario generation method is more robust to model "overfitting" by utilising a multiple of linear regression functions, hence our scenarios are more reliable. Finally, our scenario generation method enables parsimonious incorporation of decision analysis, such as worst case scenarios, hence our scenario generation facilitates decision making. This paper will also be of interest to industry professionals.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 6 ; Year: 2019 ; Pages: 1-12 ; Amsterdam: Elsevier
- Klassifikation
-
Wirtschaft
- Thema
-
Simple linear regression
Performance management
Scenario generation
Stochastic programming
Forecasting
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Mitra, Sovan
Limb, Sungmook
Karathanasopoulos, Andreas
- Ereignis
-
Veröffentlichung
- (wer)
-
Elsevier
- (wo)
-
Amsterdam
- (wann)
-
2019
- DOI
-
doi:10.1016/j.orp.2018.100095
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Mitra, Sovan
- Limb, Sungmook
- Karathanasopoulos, Andreas
- Elsevier
Entstanden
- 2019
Ähnliche Objekte (12)
Dynamic Generation of Scenario Trees
Shape-based Scenario Generation using Copulas
Optimal scenario generation and reduction in stochastic programming
Evaluation of scenario-generation methods for stochastic programming
Scenario-Driven Data Generation with Experimentable Digital Twins
Scenario-Driven Data Generation with Experimentable Digital Twins
Normalizing flow-based scenario generation for energy system optimization
Regression : models, methods and applications
Regression : models, methods and applications
Scenario
Learning scenario-specific vehicle motion models for intelligent infrastructure applications
Regression analysis : theory, methods, and applications
Dynamic Generation of Scenario Trees
Shape-based Scenario Generation using Copulas
Optimal scenario generation and reduction in stochastic programming
Evaluation of scenario-generation methods for stochastic programming
Scenario-Driven Data Generation with Experimentable Digital Twins
Scenario-Driven Data Generation with Experimentable Digital Twins
Normalizing flow-based scenario generation for energy system optimization
Regression : models, methods and applications
Regression : models, methods and applications
Scenario
Learning scenario-specific vehicle motion models for intelligent infrastructure applications
Regression analysis : theory, methods, and applications
Dynamic Generation of Scenario Trees
Shape-based Scenario Generation using Copulas
Optimal scenario generation and reduction in stochastic programming
Evaluation of scenario-generation methods for stochastic programming
Scenario-Driven Data Generation with Experimentable Digital Twins
Scenario-Driven Data Generation with Experimentable Digital Twins
Normalizing flow-based scenario generation for energy system optimization
Regression : models, methods and applications
Regression : models, methods and applications
Scenario
Learning scenario-specific vehicle motion models for intelligent infrastructure applications