期刊名称：Brazilian Journal of Probability and Statistics
印刷版ISSN：0103-0752
出版年度：2014
卷号：28
期号：1
页码：109-139
DOI：10.1214/12-BJPS198
语种：English
出版社：Brazilian Statistical Association
摘要：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.
