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February 2014 A product-multinomial framework for categorical data analysis with missing responses
Frederico Z. Poleto, Julio M. Singer, Carlos Daniel Paulino
Braz. J. Probab. Stat. 28(1): 109-139 (February 2014). DOI: 10.1214/12-BJPS198

Abstract

With the objective of analysing categorical data with missing responses, we extend the multinomial modelling scenario described by Paulino (Braz. J. Probab. Stat. 5 (1991) 1–42) to a product-multinomial framework that allows the inclusion of explanatory variables. We consider maximum likelihood (ML) and weighted least squares (WLS) as well as a hybrid ML/WLS approach to fit linear, log-linear and more general functional linear models under ignorable and nonignorable missing data mechanisms. We express the results in an unified matrix notation that may be easily used for their computational implementation and develop such a set of subroutines in R. We illustrate the procedures with the analysis of two data sets, and perform simulations to assess the properties of the estimators.

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Frederico Z. Poleto. Julio M. Singer. Carlos Daniel Paulino. "A product-multinomial framework for categorical data analysis with missing responses." Braz. J. Probab. Stat. 28 (1) 109 - 139, February 2014. https://doi.org/10.1214/12-BJPS198

Information

Published: February 2014
First available in Project Euclid: 5 February 2014

zbMATH: 06291464
MathSciNet: MR3165432
Digital Object Identifier: 10.1214/12-BJPS198

Rights: Copyright © 2014 Brazilian Statistical Association

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Vol.28 • No. 1 • February 2014
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