Add to ORKGA class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. The dependency structure is the one described by the Bayesian networks. This class allows ratio type estimators as a subclass identified by a particular dependency structure. It will be shown by a MonteCarlo simulation how the adoption of the estimator corresponding to the population structure is more efficient than the others. It will also be underlined how this class adapts to the problem of integration of information from two surveys through the probability updating system of the Bayesian networks
Data from large surveys are often supplemented with sampling weights that are designed to reflect un...
The association structure of a Bayesian network can be known in advance by subject matter knowledge...
The idea of coherence of estimates is crucial in the production process of the national institutes ...
A class of estimators based on the dependency structure of a multivariate variable of interest and t...
A class of estimators based on the dependency structure of a multivariate variable of interest and ...
A class of estimators based on the dependency structure of a multivariate variable of interest and t...
We propose a novel methodology based on the concept of Bayesian network (BN, see Cowell et al., 1999...
Bayesian networks are multivariate statistical models satisfying sets of conditional independence s...
Nowadays there is increasing availability of good quality official statistics data. The construction...
In this paper recent results about the application of Bayesian networks to official statistics are p...
In this work an estimator of the joint frequency distribution is defined when the sample is drawn ac...
Nowadays there is increasing availability of good quality official statistics data. The constructio...
Bayesian Networks (BNs) are multivariate statistical models satisfying sets of conditional independe...
This paper addresses the implementation of Bayesian sampling methodology in a graphical probability ...
Statistical matching aims at combining information obtained from different non-overlapping sample su...
Data from large surveys are often supplemented with sampling weights that are designed to reflect un...
The association structure of a Bayesian network can be known in advance by subject matter knowledge...
The idea of coherence of estimates is crucial in the production process of the national institutes ...
A class of estimators based on the dependency structure of a multivariate variable of interest and t...
A class of estimators based on the dependency structure of a multivariate variable of interest and ...
A class of estimators based on the dependency structure of a multivariate variable of interest and t...
We propose a novel methodology based on the concept of Bayesian network (BN, see Cowell et al., 1999...
Bayesian networks are multivariate statistical models satisfying sets of conditional independence s...
Nowadays there is increasing availability of good quality official statistics data. The construction...
In this paper recent results about the application of Bayesian networks to official statistics are p...
In this work an estimator of the joint frequency distribution is defined when the sample is drawn ac...
Nowadays there is increasing availability of good quality official statistics data. The constructio...
Bayesian Networks (BNs) are multivariate statistical models satisfying sets of conditional independe...
This paper addresses the implementation of Bayesian sampling methodology in a graphical probability ...
Statistical matching aims at combining information obtained from different non-overlapping sample su...
Data from large surveys are often supplemented with sampling weights that are designed to reflect un...
The association structure of a Bayesian network can be known in advance by subject matter knowledge...
The idea of coherence of estimates is crucial in the production process of the national institutes ...