1,720,968 research outputs found
Metodologia e software per l’imputazione di dati mancanti tramite le reti bayesiane
No full textImputation by Bayesian networks seems a very natural imputation process.
In fact, imputation by Bayesian networks mimes the data generation process through
the available sample information, under the MAR assumption. A review
regarding what a Bayesian network is, the model simplification induced by its
definition, and what methodologies have been defined up to now, is provided.
Finally, a software implementing imputation procedures by Bayesian networks is
presented
Some approaches in imputing missing items with Bayesian Networks
No full textIn questo lavoro vengono poste a confronto due tecniche, basate sulle reti
Bayesiane, per l’imputazione di dati mancanti. Il confronto viene condotto tramite un
esperimento su dati del censimento della popolazione del Regno Unito
Population Size Estimation Using Multiple Incomplete Lists with Overcoverage
Full text linkThe quantity and quality of administrative information available to National Statistical Institutes have been constantly increasing over the past several years. However, different sources of administrative data are not expected to each have the same population coverage, so that estimating the true population size from the collective set of data poses several methodological challenges that set the problem apart from a classical capture-recapture setting. In this article, we consider two specific aspects of this problem: (1) misclassification of the units, leading to lists with both overcoverage and undercoverage; and (2) lists focusing on a specific subpopulation, leaving a proportion of the population with null probability of being captured. We propose an approach to this problem that employs a class of capturerecapture methods based on Latent Class models. We assess the proposed approach via a simulation study, then apply the method to five sources of empirical data to estimate the number of active local units of Italian enterprises in 2011
Multivariate techniques for imputation based on Bayesian networks
No full textIn this paper, we compare two imputation procedures based on
Bayesian networks. One method imputes missing items of a variable taking
advantage only on information of its parents, while the other takes advantage
of its Markov blanket. The structure of the paper is as follows. The first
section contains an illustration of Bayesian networks. Then, we explain how
to use the information contained in Bayesian networks in section 2. In section
3, we describe two evaluation indicators of imputation procedures. Finally,
a Monte Carlo evaluation is carried on a real data set in section 4
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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