Arbeitspapier
Prediction Optimal Classification of Business Phases
Linear Discriminant Analysis (LDA) performs well for classifica- tion of business phases – even though the premises of an LDA are not met. As the variables are highly correlated there are numerical as well as interpretational shortcomings. By transforming the classification problem to a regression setting both problems can be addressed by a computer-intensive prediction oriented method which also improves the classification performance.
- Language
-
Englisch
- Bibliographic citation
-
Series: Technical Report ; No. 2005,41
- Subject
-
Konjunktur
Zeitreihenanalyse
Diskriminanzanalyse
Clusteranalyse
Theorie
- Event
-
Geistige Schöpfung
- (who)
-
Weihs, Claus
Luebke, Karsten
- Event
-
Veröffentlichung
- (who)
-
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
-
Dortmund
- (when)
-
2005
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Weihs, Claus
- Luebke, Karsten
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2005
Other Objects (12)
Multivariate classification of business phases
Business phase classification and prediction: how to compare interpretability of classification methods?
Incorporating background knowledge for better prediction of cycle phases
Classification and Clustering in Business Cycle Analysis
Business cycle prediction using support vector methods
Dynamic Bayesian networks for classification of business cycles
Importance Assessment of Correlated Predictors in Business Cycles Classification
A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification
Classification-relevant Importance Measures for the West German Business Cycle
Direct minimization of error rates in multivariate classification
Local Modelling in Classification on Different Feature Subspaces
Clustering techniques for the detection of business cycles
Multivariate classification of business phases
Business phase classification and prediction: how to compare interpretability of classification methods?
Incorporating background knowledge for better prediction of cycle phases
Classification and Clustering in Business Cycle Analysis
Business cycle prediction using support vector methods
Dynamic Bayesian networks for classification of business cycles
Importance Assessment of Correlated Predictors in Business Cycles Classification
A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification
Classification-relevant Importance Measures for the West German Business Cycle
Direct minimization of error rates in multivariate classification
Local Modelling in Classification on Different Feature Subspaces
Clustering techniques for the detection of business cycles
Multivariate classification of business phases
Business phase classification and prediction: how to compare interpretability of classification methods?
Incorporating background knowledge for better prediction of cycle phases
Classification and Clustering in Business Cycle Analysis
Business cycle prediction using support vector methods
Dynamic Bayesian networks for classification of business cycles
Importance Assessment of Correlated Predictors in Business Cycles Classification
A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification
Classification-relevant Importance Measures for the West German Business Cycle
Direct minimization of error rates in multivariate classification
Local Modelling in Classification on Different Feature Subspaces