- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Estimating observation and model error variances using multiple data sets ; year:2018 ; pages:1-38
Atmospheric measurement techniques discussions ; (2018), 1-38
- Creator
-
Anthes, Richard A.
Rieckh, Therese
- DOI
-
10.5194/amt-2017-487
- URN
-
urn:nbn:de:101:1-201801252388
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:57 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Anthes, Richard A.
- Rieckh, Therese
Other Objects (12)
Estimating observation and model error variances using multiple data sets
Evaluating two methods of estimating error variances from multiple data sets using an error model
Evaluating two methods of estimating error variances using simulated data sets with known errors
Estimating deterministically time-varying variances in regression models
Estimating high-frequency based (co-) variances: A unified approach
Estimating income variances by probability sampling: A case study
Estimating high-frequency based (co-)variances : a unified approach
Observation-driven Models for Realized Variances and Overnight Returns
Cryo-electron microscopy - estimating conformational variances by principal motion analysis
Estimating variances and covariances for bivariate animal models using scaling and transformation
Estimating variances and covariances for multivariate animal models by restricted maximum likelihood
On the estimation of the linear relation when the error variances are known
Estimating observation and model error variances using multiple data sets
Evaluating two methods of estimating error variances from multiple data sets using an error model
Evaluating two methods of estimating error variances using simulated data sets with known errors
Estimating deterministically time-varying variances in regression models
Estimating high-frequency based (co-) variances: A unified approach
Estimating income variances by probability sampling: A case study
Estimating high-frequency based (co-)variances : a unified approach
Observation-driven Models for Realized Variances and Overnight Returns
Cryo-electron microscopy - estimating conformational variances by principal motion analysis
Estimating variances and covariances for bivariate animal models using scaling and transformation
Estimating variances and covariances for multivariate animal models by restricted maximum likelihood
On the estimation of the linear relation when the error variances are known
Estimating observation and model error variances using multiple data sets
Evaluating two methods of estimating error variances from multiple data sets using an error model
Evaluating two methods of estimating error variances using simulated data sets with known errors
Estimating deterministically time-varying variances in regression models
Estimating high-frequency based (co-) variances: A unified approach
Estimating income variances by probability sampling: A case study
Estimating high-frequency based (co-)variances : a unified approach
Observation-driven Models for Realized Variances and Overnight Returns
Cryo-electron microscopy - estimating conformational variances by principal motion analysis
Estimating variances and covariances for bivariate animal models using scaling and transformation
Estimating variances and covariances for multivariate animal models by restricted maximum likelihood