Add to ORKGThe paper proposes a comparison between dynamic models with continuous and discrete latent variables for panel data. We consider Limited Dependent Variable models (LDV) in the first case, and Latent Markov (LM) models in the second case. In both cases the maximum likelihood estimation method through the EM algorithm is used. Since the likelihood of LDV models is not tractable analytically, we implemented the Gauss Hermite and the Adaptive Gauss Hermite quadrature methods for approximating the integrals involved in it. The comparison between the two classes of models is carried out by means of a simulation stud
this paper we concentrate on latent profile analysis, which corresponds to the case of discrete late...
Latent variable models have been playing a central role in psychometrics and related fields. In many...
Generalized Linear Latent Variable Models (GLLVM), as defined in Bartholomew and Knott (1999), enabl...
The paper proposes a comparison between dynamic models with continuous and discrete latent variables...
Dynamic latent variable models represent a useful and flexible tool in the study of macro and micro-...
Maximum likelihood estimation of dynamic latent variable models requires to solve integrals that are...
This dissertation contributes four essays to the broad literature on microeconometric modelling of l...
Latent structure models involve real, potentially observable variables and latent, unobservable vari...
open2noFirst Online: 13 April 2016Maximum likelihood estimation of models based on continuous latent...
In the paper Jöreskog's general static model with latent variables (LISREL) and extensively used in ...
This paper discusses estimation methods for limited dependent variable (LDV) models that employ Mont...
Generalized Linear Latent Variable Models (GLLVM), as defined in Bartholomew and Knott (1999) allow ...
Owing to the nature of the problems and the design of questionnaires, discrete polytomous data are v...
Latent variable models have been widely applied in different fields of research in which the con- st...
Learning the structure of graphical models is an important task, but one of considerable difficulty ...
this paper we concentrate on latent profile analysis, which corresponds to the case of discrete late...
Latent variable models have been playing a central role in psychometrics and related fields. In many...
Generalized Linear Latent Variable Models (GLLVM), as defined in Bartholomew and Knott (1999), enabl...
The paper proposes a comparison between dynamic models with continuous and discrete latent variables...
Dynamic latent variable models represent a useful and flexible tool in the study of macro and micro-...
Maximum likelihood estimation of dynamic latent variable models requires to solve integrals that are...
This dissertation contributes four essays to the broad literature on microeconometric modelling of l...
Latent structure models involve real, potentially observable variables and latent, unobservable vari...
open2noFirst Online: 13 April 2016Maximum likelihood estimation of models based on continuous latent...
In the paper Jöreskog's general static model with latent variables (LISREL) and extensively used in ...
This paper discusses estimation methods for limited dependent variable (LDV) models that employ Mont...
Generalized Linear Latent Variable Models (GLLVM), as defined in Bartholomew and Knott (1999) allow ...
Owing to the nature of the problems and the design of questionnaires, discrete polytomous data are v...
Latent variable models have been widely applied in different fields of research in which the con- st...
Learning the structure of graphical models is an important task, but one of considerable difficulty ...
this paper we concentrate on latent profile analysis, which corresponds to the case of discrete late...
Latent variable models have been playing a central role in psychometrics and related fields. In many...
Generalized Linear Latent Variable Models (GLLVM), as defined in Bartholomew and Knott (1999), enabl...