Paradata and Bayesian networks: a tool for monitoring and troubleshooting the data production process

The problem of monitoring and managing the data production process by means of process flow indicators is presented in a decision theory framework. Here it is shown how to represent and solve the decision problem via influence diagrams, i.e. Bayesian network supporting decisions. An illustrative example is provided.Expected utility, graphical models, probability update,

Model assisted approaches to complex survey sampling from finite populations using Bayesian Networks

A 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 Monte Carlo 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 probability updating system of the Bayesian networks.Graphical models, probability update, survey design

Object-oriented Bayesian networks for a decision support system for antitrust enforcement

We study an economic decision problem where the actors are two firms and the Antitrust Authority whose main task is to monitor and prevent firms' potential anti-competitive behaviour and its effect on the market. The Antitrust Authority's decision process is modelled using a Bayesian network where both the relational structure and the parameters of the model are estimated from a data set provided by the Authority itself. A number of economic variables that influence this decision process are also included in the model. We analyse how monitoring by the Antitrust Authority affects firms' strategies about cooperation. Firms' strategies are modelled as a repeated prisoner's dilemma using object-oriented Bayesian networks. We show how the integration of firms' decision process and external market information can be modelled in this way. Various decision scenarios and strategies are illustrated

Object-Oriented Bayesian Networks for a Decision Support System

We study an economic decision problem where the actors are two rms and the Antitrust Authority whose main task is to monitor and prevent rms potential anti-competitive behaviour. The Antitrust Au- thority's decision process is modelled using a Bayesian network whose relational structure and parameters are estimated from data provided by the Authority itself. Several economic variables in uencing this de- cision process are included in the model. We analyse how monitoring by the Antitrust Authority aects rms cooperation strategies. These are modelled as a repeated prisoners dilemma using object-oriented Bayesian networks, thus enabling integration of rms decision process and external market information.Antitrust Authority, Bayesian networks, mergers, model integration, prisoners dilemma, repeated games.

On the estimation of the Lorenz curve under complex sampling designs

This paper focuses on the estimation of the concentration curve of a finite population, when data are collected according to a complex sampling design with different inclusion probabilities. A (design-based) Hajek type estimator for the Lorenz curve is proposed, and its asymptotic properties are studied. Then, a resampling scheme able to approximate the asymptotic law of the Lorenz curve estimator is constructed. Applications are given to the construction of (i) a confidence band for the Lorenz curve, (ii) confidence intervals for the Gini concentration ratio, and (iii) a test for Lorenz dominance. The merits of the proposed resampling procedure are evaluated through a simulation study

Can Bayesian Network empower propensity score estimation from Real World Data?

A new method, based on Bayesian Networks, to estimate propensity scores is proposed with the purpose to draw causal inference from real world data on the average treatment effect in case of a binary outcome and discrete covariates. The proposed method ensures maximum likelihood properties to the estimated propensity score, i.e. asymptotic efficiency, thus outperforming other available approach. Two point estimators via inverse probability weighting are then proposed, and their main distributional properties are derived for constructing confidence interval and for testing the hypotheses of absence of the treatment effect. Empirical evidence of the substantial improvements offered by the proposed methodology versus standard logistic modelling of propensity score is provided in simulation settings that mimic the characteristics of a real dataset of prostate cancer patients from Milan San Raffaele Hospital

Applications of Bayesian networks in official statistics

this paper is devoted to the use of Bayesian networks in topics of central interest in official statistics. In particular we study problems related to: missing item imputation; estimation of contingency tables in complex survey sampling; quality of data production process

New statistical approaches for estimating mutation parameters

In questo lavoro vengono presentate due metodologie per la stima del tasso di mutazione genetica. La stima viene effettuata sulla base di dati raccolti presso i laboratori forensi, relativi a casi di paternit`a discussa. Vengono eveidenziati quei fattori, quali paternit` a incerta e mutazioni nascoste, che complicano la stima del tasso di mutazione. Le metodologie proposte tengono conto di questi fattori: la prima usa esclusivamente misure di conteggio mentre la seconda `e sviluppata mediante le reti bayesiane

On the identification of a single-factor model with correlated residuals

A necessary and sufficient condition for the identification of a single-factor model with correlated residuals is derived by studying the zero elements of their concentration matrix. In particular the new condition is expressed in terms of the complementary graph of the residuals. This graphical condition can be simply checked by means of an efficient algorithm. An example is also given showing that sometimes the single-factor model with correlated residuals can be sued to overcome the problem of non-identification of a larger factor model

Causal discovery for complex survey data

The association structure of a Bayesian network can be drawn based on subject matter or experts knowledge, or have to be learned from a database. In case of data driven learning, one of the most known procedures is the PC algorithm that is based on the assumption of independent and identically distributed observations. In practice, sample selection in surveys involves more complex sampling designs then the standard test procedure is not valid even asymptotically. In order to avoid misleading results about the true causal structure the sample selection process must be taken into account in the structural learning process. In this paper, a modified version of the PC algorithm is proposed for inferring casual structure from complex survey data. Finally, a simulation experiment is performed