Arbeitspapier
Sliced inverse regression for high-dimensional time series
Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which was developed to reduce the dimension of the regressor in regression problems, as an exploratory tool for analyzing multivariate time series. Analyzing each variable individually, we search for those directions, i.e., linear combinations of past and present observations of the other variables which explain most of the variability of the variable considered. This can also provide information on possible nonlinearities. We apply a dynamic version of SIR to multivariate physiological time series observed in intensive care.
- Language
-
Englisch
- Bibliographic citation
-
Series: Technical Report ; No. 2001,14
- Subject
-
Time series analysis
Nonlinearities
Dimension Reduction
- Event
-
Geistige Schöpfung
- (who)
-
Becker, Claudia
Fried, Roland
- Event
-
Veröffentlichung
- (who)
-
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
-
Dortmund
- (when)
-
2001
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
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
- Becker, Claudia
- Fried, Roland
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2001
Other Objects (12)
Sliced Inverse Regression for High-dimensional Time Series
Variance estimation for high-dimensional regression models
Variance Estimation for High-Dimensional Regression Models
Regression Models for High-Dimensional, Biological Data
Estimating High-Dimensional Time Series Models.
Adaptive bridge estimation for high-dimensional regression models
L1-Penalised quantile regression in high-dimensional sparse models
Inference of breakpoints in high-dimensional time series
Confidence Intervals in High-Dimensional Regression Based on Regularized Pseudoinverses
Robust inference in high-dimensional approximately sparse quantile regression models
High-dimensional sparse financial networks through a regularised regression model
Post-l1-penalized estimators in high-dimensional linear regression models
Sliced Inverse Regression for High-dimensional Time Series
Variance estimation for high-dimensional regression models
Variance Estimation for High-Dimensional Regression Models
Regression Models for High-Dimensional, Biological Data
Estimating High-Dimensional Time Series Models.
Adaptive bridge estimation for high-dimensional regression models
L1-Penalised quantile regression in high-dimensional sparse models
Inference of breakpoints in high-dimensional time series
Confidence Intervals in High-Dimensional Regression Based on Regularized Pseudoinverses
Robust inference in high-dimensional approximately sparse quantile regression models
High-dimensional sparse financial networks through a regularised regression model
Post-l1-penalized estimators in high-dimensional linear regression models
Sliced Inverse Regression for High-dimensional Time Series
Variance estimation for high-dimensional regression models
Variance Estimation for High-Dimensional Regression Models
Regression Models for High-Dimensional, Biological Data
Estimating High-Dimensional Time Series Models.
Adaptive bridge estimation for high-dimensional regression models
L1-Penalised quantile regression in high-dimensional sparse models
Inference of breakpoints in high-dimensional time series
Confidence Intervals in High-Dimensional Regression Based on Regularized Pseudoinverses
Robust inference in high-dimensional approximately sparse quantile regression models
High-dimensional sparse financial networks through a regularised regression model