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  • 1. Joint determination of optimal stratification and sample allocation using genetic algorithm Archived
    Articles and reports: 12-001-X201300211884
    Description:

    This paper offers a solution to the problem of finding the optimal stratification of the available population frame, so as to ensure the minimization of the cost of the sample required to satisfy precision constraints on a set of different target estimates. The solution is searched by exploring the universe of all possible stratifications obtainable by cross-classifying the categorical auxiliary variables available in the frame (continuous auxiliary variables can be transformed into categorical ones by means of suitable methods). Therefore, the followed approach is multivariate with respect to both target and auxiliary variables. The proposed algorithm is based on a non deterministic evolutionary approach, making use of the genetic algorithm paradigm. The key feature of the algorithm is in considering each possible stratification as an individual subject to evolution, whose fitness is given by the cost of the associated sample required to satisfy a set of precision constraints, the cost being calculated by applying the Bethel algorithm for multivariate allocation. This optimal stratification algorithm, implemented in an R package (SamplingStrata), has been so far applied to a number of current surveys in the Italian National Institute of Statistics: the obtained results always show significant improvements in the efficiency of the samples obtained, with respect to previously adopted stratifications.

    Release date: 2014-01-15
  • 2. Model-assisted approaches to complex survey sampling from finite populations using Bayesian networks: a tool for integration of different sources Archived
    Articles and reports: 11-522-X20050019466
    Description:

    A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. 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.

    Release date: 2007-03-02
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Articles and reports (2)

Articles and reports (2) ((2 results))

  • 1. Joint determination of optimal stratification and sample allocation using genetic algorithm Archived
    Articles and reports: 12-001-X201300211884
    Description:

    This paper offers a solution to the problem of finding the optimal stratification of the available population frame, so as to ensure the minimization of the cost of the sample required to satisfy precision constraints on a set of different target estimates. The solution is searched by exploring the universe of all possible stratifications obtainable by cross-classifying the categorical auxiliary variables available in the frame (continuous auxiliary variables can be transformed into categorical ones by means of suitable methods). Therefore, the followed approach is multivariate with respect to both target and auxiliary variables. The proposed algorithm is based on a non deterministic evolutionary approach, making use of the genetic algorithm paradigm. The key feature of the algorithm is in considering each possible stratification as an individual subject to evolution, whose fitness is given by the cost of the associated sample required to satisfy a set of precision constraints, the cost being calculated by applying the Bethel algorithm for multivariate allocation. This optimal stratification algorithm, implemented in an R package (SamplingStrata), has been so far applied to a number of current surveys in the Italian National Institute of Statistics: the obtained results always show significant improvements in the efficiency of the samples obtained, with respect to previously adopted stratifications.

    Release date: 2014-01-15
  • 2. Model-assisted approaches to complex survey sampling from finite populations using Bayesian networks: a tool for integration of different sources Archived
    Articles and reports: 11-522-X20050019466
    Description:

    A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. 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.

    Release date: 2007-03-02
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