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  • We derive explicit formulae for estimation in logistic regression models where some of thecovariates are missing. Our approach allows for modelling the distribution of the missing covariateseither as a multivariate normal or as a multivariate t-distribution. A main advantage of this methodis that it is fast and does not require the use of iterative procedures. A model selection methodis derived which allows to choose among these distributions. In addition, we consider versions ofAkaikeÂ’s information criterion that are based on the expectation–maximization algorithm and multipleimputation methods that have a wide applicability to model selection in likelihood models in general. (xsd:string)
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  • (EVS) (xsd:string)
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  • 2011 (xsd:gyear)
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  • 2011 (xsd:gyear)
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  • 159 (xsd:string)
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  • 2 (xsd:string)
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  • Missing covariates in logistic regression, estimationand distribution selection (xsd:string)
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  • article (xsd:string)
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  • In Statistical Modelling, 11(2), 159-183, 2011 (xsd:string)
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  • European Values Study (EVS) (xsd:string)
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  • 2011 (xsd:string)
  • Akaike_information_criterion (xsd:string)
  • EVS (xsd:string)
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  • 183 (xsd:string)
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  • 11 (xsd:string)