Model Selection for Extended Quasi-Likelihood Models in Small Samples

Biometrics ◽  
1995 ◽  
Vol 51 (3) ◽  
pp. 1077 ◽  
Author(s):  
Clifford M. Hurvich ◽  
Chih-Ling Tsai
Keyword(s):  
Model Selection ◽  
Small Samples ◽  
Selection For

Model Selection for Multivariate Regression in Small Samples

Biometrics ◽  
1994 ◽  
Vol 50 (1) ◽  
pp. 226 ◽  
Author(s):  
Edward J. Bedrick ◽  
Chih-Ling Tsai
Keyword(s):  
Model Selection ◽  
Multivariate Regression ◽  
Small Samples ◽  
Selection For

scholarly journals Criteria for longitudinal data model selection based on Kullback’s symmetric divergence

2012 ◽  
Vol Volume 15, 2012 ◽  
Author(s):  
Bezza Hafidi ◽  
Nourddine Azzaoui
Keyword(s):  
Model Selection ◽  
Longitudinal Data ◽  
Data Model ◽  
Small Samples ◽  
Correlated Errors ◽  
Residual Likelihood ◽  
International Audience ◽  
Selection For ◽  
Aic Criterion ◽  
New Criteria

International audience Recently, Azari et al (2006) showed that (AIC) criterion and its corrected versions cannot be directly applied to model selection for longitudinal data with correlated errors. They proposed two model selection criteria, AICc and RICc, by applying likelihood and residual likelihood approaches. These two criteria are estimators of the Kullback-Leibler's divergence distance which is asymmetric. In this work, we apply the likelihood and residual likelihood approaches to propose two new criteria, suitable for small samples longitudinal data, based on the Kullback's symmetric divergence. Their performance relative to others criteria is examined in a large simulation study

scholarly journals Optimization of Aerosol Model Selection for TROPOMI/S5P

2021 ◽  
Vol 13 (13) ◽  
pp. 2489
Author(s):  
Lanlan Rao ◽  
Jian Xu ◽  
Dmitry S. Efremenko ◽  
Diego G. Loyola ◽  
Adrian Doicu
Keyword(s):  
Model Selection ◽  
Insufficient Information ◽  
Satellite Measurements ◽  
Model Uncertainties ◽  
Bayesian Algorithm ◽  
Layer Height ◽  
Aerosol Model ◽  
Selection For ◽  
Temporal Inhomogeneity ◽  
Aerosol Properties

To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.

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