A general definition of a conditional model is proposed through embedding that concept into the usual concept of a standard statistical model. The identification of a conditional model is eventually analysed in the framework of the identification of a function of the parameters in a standard statistical model. A simple example shows some practical implications of the proposed approach. Whereas this paper is written in a pure sampling theory framework, a final remark connects this framework with the bayesian approach to the identification in conditional models
This article is concerned with local identification of individual parameters of dynamic stochastic g...
The problem of identifiability is basic to all statistical methods and data analysis, occurring in s...
After introducing, at the level of model specication, three basic distinctions {the rst one between ...
This paper contributes to the construction of a general theory for conditional models by making expl...
Abstract: In this thesis, we give a general construction of a conditional model through embedding th...
International audienceThis paper studies the role played by identification in the Bayesian analysis ...
This note argues that a bayesian framework is almost inescapable when specifying statistical models ...
The paper aims at systematic placement of identification concept within Bayesian approach. Pointing ...
The fiducial is not unique in general, but we prove that in a restricted class of models it is uniqu...
The problem of identification is defined in terms of the possibility of characterizing parameters of...
When one wants to estimate a model without specifying the functions and distributions parametrically...
Identification in econometric models maps prior assumptions and the data to information about a para...
This paper considers how the concepts of likelihood and identification became part of Bayesian theor...
Introduction Identification What Is against Us? What Is for Us? Some Simple Examples of Partially Id...
Identification can be a major issue in causal modeling contexts, and in contexts where observational...
This article is concerned with local identification of individual parameters of dynamic stochastic g...
The problem of identifiability is basic to all statistical methods and data analysis, occurring in s...
After introducing, at the level of model specication, three basic distinctions {the rst one between ...
This paper contributes to the construction of a general theory for conditional models by making expl...
Abstract: In this thesis, we give a general construction of a conditional model through embedding th...
International audienceThis paper studies the role played by identification in the Bayesian analysis ...
This note argues that a bayesian framework is almost inescapable when specifying statistical models ...
The paper aims at systematic placement of identification concept within Bayesian approach. Pointing ...
The fiducial is not unique in general, but we prove that in a restricted class of models it is uniqu...
The problem of identification is defined in terms of the possibility of characterizing parameters of...
When one wants to estimate a model without specifying the functions and distributions parametrically...
Identification in econometric models maps prior assumptions and the data to information about a para...
This paper considers how the concepts of likelihood and identification became part of Bayesian theor...
Introduction Identification What Is against Us? What Is for Us? Some Simple Examples of Partially Id...
Identification can be a major issue in causal modeling contexts, and in contexts where observational...
This article is concerned with local identification of individual parameters of dynamic stochastic g...
The problem of identifiability is basic to all statistical methods and data analysis, occurring in s...
After introducing, at the level of model specication, three basic distinctions {the rst one between ...