Minimum phi-divergence estimators for multinomial logistic regression with complex sample design

Elena Castilla, Nirian Martín and Leandro Pardo ()
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Elena Castilla: Complutense University of Madrid
Nirian Martín: Complutense University of Madrid
Leandro Pardo: Complutense University of Madrid

AStA Advances in Statistical Analysis, 2018, vol. 102, issue 3, No 4, 411 pages

Abstract: Abstract This article develops the theoretical framework needed to study the multinomial regression model for complex sample design with pseudo-minimum phi-divergence estimators. The numerical example and the simulation study propose new estimators for the parameter of the logistic regression with overdispersed multinomial distributions for the response variables, the pseudo-minimum Cressie–Read divergence estimators, as well as new estimators for the intra-cluster correlation coefficient. The simulation study shows that the Binder’s method for the intra-cluster correlation coefficient exhibits an excellent performance when the pseudo-minimum Cressie–Read divergence estimator, with $$\lambda =\frac{2}{3}$$ λ = 2 3 , is plugged.

Keywords: Design effect; Cluster sampling; Pseudo-likelihood; Sample weight; 62F12; 62J12 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10182-017-0311-6

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