Many economic applications require to integrate information coming from different data sources. In this work we consider a specific integration problem called statistical matching, referring to probabilistic distributions of Y|X, Z|X and X, where X, Y, Z are categorical (possibly multi-dimensional) random variables. Here, we restrict to the case of no logical relations among random variables X, Y, Z. The non-uniqueness of the conditional distribution of (Y, Z)|X suggests to deal with sets of probabilities. For that we consider different strategies to get a conditional belief function for (Y, Z)|X that approximates the initial assessment in a reasonable way. In turn, such conditional belief function, together with the marginal probability distribution of X, gives rise to a joint belief function for the distribution of V=(X,Y,Z) .

Dempster-Shafer Approximations and Probabilistic Bounds in Statistical Matching / Petturiti, Davide; Vantaggi, Barbara. - (2021), pp. 367-380. - LECTURE NOTES IN ARTIFICIAL INTELLIGENCE. [10.1007/978-3-030-86772-0_27].

Dempster-Shafer Approximations and Probabilistic Bounds in Statistical Matching

Vantaggi, Barbara
2021

Abstract

Many economic applications require to integrate information coming from different data sources. In this work we consider a specific integration problem called statistical matching, referring to probabilistic distributions of Y|X, Z|X and X, where X, Y, Z are categorical (possibly multi-dimensional) random variables. Here, we restrict to the case of no logical relations among random variables X, Y, Z. The non-uniqueness of the conditional distribution of (Y, Z)|X suggests to deal with sets of probabilities. For that we consider different strategies to get a conditional belief function for (Y, Z)|X that approximates the initial assessment in a reasonable way. In turn, such conditional belief function, together with the marginal probability distribution of X, gives rise to a joint belief function for the distribution of V=(X,Y,Z) .
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1580126
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