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Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables ("statistical consistency") and preservation of logical constraints in data ("logical consistency"). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census. Copyright 2004 Royal Statistical Society.

Most National Statistical Institutes are progressively moving from traditional production models to new strategies based on the combined use of different sources of information, which can be both primary and secondary. In this article, we propose a framework for assessing the quality of multisource processes, such as statistical registers. The final aim is to develop a tool supporting decisions about the process design and its monitoring, and to provide quality measures of the whole production. The starting point is the adaptation of the life-cycle paradigm, that results in a three-phases framework described in recent literature. An evolution of this model is proposed, focusing on the first two phases of the life-cycle, to better represent the source integration/combination phase, that can vary accordingly to the features of different types of processes. The proposed enhancement would improve the existing quality framework to support the evaluation of different multisource processes. An application of the proposed framework to two Istat (Italian national statistical institute) registers in the economic area taken as case studies is presented. These experiences show the potentials of such tool in supporting National Statistical Institutes in assessing multisource statistical production processes.

In recent years, the Italian national institute of statistics (Istat), together with most National Statistical Institutes, is progressively moving from traditional production models based on the use of primary source of information - represented by direct surveys - to new production strategies based on the combined use of different primary and secondary sources of information. As result, new multisource statistical processes have been built, that guarantee a major improvement of both amount and quality of information about several phenomena of public interest. In this context, the Total Process Error (TPE) framework has been recently proposed in literature for assessing the quality of multisource processes. The TPE framework represents an evolution of the Zhang’s two-phase life-cycle approach and it additionally includes an operational tool to connect the steps of the multisource production process to the phases of the quality evaluation framework. TPE framework can be used both to support a multisource process design and to monitor an entire production process, in order to provide key elements to assess the quality of both the processes and their statistical outputs. In the present work, we describe as a case study in the new context of Istat production of official statistics the use of the TPE framework to support the process design of the Register for Public Administrations.

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