Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package ipcr
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: Psych, Band 3, Ausgabe 3, Seite 360-385, 2021
- Event
-
Veröffentlichung
- (where)
-
Berlin
- (who)
-
Humboldt-Universität zu Berlin
- (when)
-
2021
- Creator
-
Arnold, Manuel
Brandmaier, Andreas
Voelkle, Manuel
- DOI
-
10.18452/23516
- URN
-
urn:nbn:de:kobv:11-110-18452/24167-3
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
2025-03-25T13:53:36+0100
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
- Arnold, Manuel
- Brandmaier, Andreas
- Voelkle, Manuel
- Humboldt-Universität zu Berlin
Time of origin
- 2021
Other Objects (12)
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
SPOTting model parameters using a ready-made python package
Equivalence of regression curves sharing common parameters
Equivalence of regression curves sharing common parameters
Random Parameters and Spatial Heterogeneity using Rchoice package in R
EFFICIENT TWO-PARAMETER ESTIMATOR IN LINEAR REGRESSION MODEL
Logistische Regression und Diskrimination bei zufällig variierenden Parametern
Parameter estimation in the random effects meta regression model
Estimating Structural Parameters in Regression Models with Adaptive Learning
Estimating Structural Parameters in Regression Models with Adaptive Learning
Predicting perceptual voice quality from objective voice parameters in dysphonic patients
Predicting volatility based on interval regression models
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
SPOTting model parameters using a ready-made python package
Equivalence of regression curves sharing common parameters
Equivalence of regression curves sharing common parameters
Random Parameters and Spatial Heterogeneity using Rchoice package in R
EFFICIENT TWO-PARAMETER ESTIMATOR IN LINEAR REGRESSION MODEL
Logistische Regression und Diskrimination bei zufällig variierenden Parametern
Parameter estimation in the random effects meta regression model
Estimating Structural Parameters in Regression Models with Adaptive Learning
Estimating Structural Parameters in Regression Models with Adaptive Learning
Predicting perceptual voice quality from objective voice parameters in dysphonic patients
Predicting volatility based on interval regression models
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
SPOTting model parameters using a ready-made python package
Equivalence of regression curves sharing common parameters
Equivalence of regression curves sharing common parameters
Random Parameters and Spatial Heterogeneity using Rchoice package in R
EFFICIENT TWO-PARAMETER ESTIMATOR IN LINEAR REGRESSION MODEL
Logistische Regression und Diskrimination bei zufällig variierenden Parametern
Parameter estimation in the random effects meta regression model
Estimating Structural Parameters in Regression Models with Adaptive Learning
Estimating Structural Parameters in Regression Models with Adaptive Learning
Predicting perceptual voice quality from objective voice parameters in dysphonic patients