Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2015

Multinomial logistic estimation in dual frame surveys

  • Identification data

    Identifier: RP:2436
    Handle: https://hdl.handle.net/20.500.11797/RP2436
    Authors:
    Ranalli, Maria GiovannaArcos, AntonioRueda, Maria del MarMolina, David
    Abstract:
    We consider estimation techniques from dual frame surveys in the case of estimation of proportions when the variable of interest has multinomial outcomes. We propose to describe the joint distribution of the class indicators by a multinomial logistic model. Logistic generalized regression estimators and model calibration estimators are introduced for class frequencies in a population. Theoretical asymptotic properties of the proposed estimators are shown and discussed. Monte Carlo experiments are also carried out to compare the efficiency of the proposed procedures for finite size samples and in the presence of different sets of auxiliary variables. The simulation studies indicate that the multinomial logistic formulation yields better results than the classical estimators that implicitly assume individual linear models for the variables. The proposed methods are also applied in an attitude survey.

  • Others:

    URV's Author/s: Ranalli, Maria Giovanna Arcos, Antonio Rueda, Maria del Mar Molina, David
    Keywords: Finite population, survey sampling, auxiliary information, model assisted inference, calibration
    Abstract: We consider estimation techniques from dual frame surveys in the case of estimation of proportions when the variable of interest has multinomial outcomes. We propose to describe the joint distribution of the class indicators by a multinomial logistic model. Logistic generalized regression estimators and model calibration estimators are introduced for class frequencies in a population. Theoretical asymptotic properties of the proposed estimators are shown and discussed. Monte Carlo experiments are also carried out to compare the efficiency of the proposed procedures for finite size samples and in the presence of different sets of auxiliary variables. The simulation studies indicate that the multinomial logistic formulation yields better results than the classical estimators that implicitly assume individual linear models for the variables. The proposed methods are also applied in an attitude survey.
    Journal publication year: 2015
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Finite population, survey sampling, auxiliary information, model assisted inference, calibration

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