Add to ORKGModel selection criteria in the presence of missing data based on the Kullback-Leibler discrepanc
The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assessm...
This paper considers model selection in nonlinear panel data models where incidental parameters or l...
Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical mod...
[[abstract]]We consider penalized likelihood criteria for selecting models of dependent processes. T...
We consider novel methods for the computation of model selection criteria in missing-data problems b...
Model selection is a critical part of analysis of data in applied research. Equally ubiquitous is th...
Model selection is of fundamental importance to high dimensional modelling featured in many contempo...
This article describes a new approach to Bayesian selection of decomposabl e models with incomplete ...
This paper considers model selection in panels where incidental parameters are present. Primary inte...
Summary: We explore the use of a posterior predictive loss criterion for model selection for incompl...
The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assess...
We propose a procedure associated with the idea of the E-M algorithm for model selection in the pres...
When data are incomplete, models are often catalogued according to one of the three modelling framew...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assessm...
The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assessm...
This paper considers model selection in nonlinear panel data models where incidental parameters or l...
Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical mod...
[[abstract]]We consider penalized likelihood criteria for selecting models of dependent processes. T...
We consider novel methods for the computation of model selection criteria in missing-data problems b...
Model selection is a critical part of analysis of data in applied research. Equally ubiquitous is th...
Model selection is of fundamental importance to high dimensional modelling featured in many contempo...
This article describes a new approach to Bayesian selection of decomposabl e models with incomplete ...
This paper considers model selection in panels where incidental parameters are present. Primary inte...
Summary: We explore the use of a posterior predictive loss criterion for model selection for incompl...
The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assess...
We propose a procedure associated with the idea of the E-M algorithm for model selection in the pres...
When data are incomplete, models are often catalogued according to one of the three modelling framew...
A model selection criterion is often formulated by constructing an approx-imately unbiased estimator...
The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assessm...
The deviance information criterion (DIC) introduced by Spiegelhalter et al. (2002) for model assessm...
This paper considers model selection in nonlinear panel data models where incidental parameters or l...
Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical mod...