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## Contributions: Abstract## Marginal regression models for incomplete ordinal response variables
We outline a likelihood-based regression model appropriate foranalyzing incomplete multivariate categorical responses. When thenon-response mechanism is Log-linear models are usually used in this cases, considering anaugmented 2 We consider two extensions to the classical formulation ofthe link function particularly relevant for ordinal responsevariables. In addition to local and global logits, continuation orreverse continuation logits could also be appropriate for certainvariables, so that four different types of logit can be used tomodel univariate marginal distributions. The second extension allows models to be defined also by inequality constraints, arising especially in testing stochastic orderings, positive association between pairs of variables or when the degree of dependence increases with respect to a set of covariates. The problem of boundary solutions in non-ignorable models is well known in literature. Models with inequality constraints on the missing-data mechanism are also useful in avoiding this problem. The likelihood function is maximized via the EM algorithm. Wepropose for the E step a useful matrix formulation valid forgeneral patterns of non responses. For the M step we present theFisher scoring algorithm and the Aitchison-Silvey algorithmextended to involve equality and inequality constraints on thevector of marginal parameters. Finally, we present an application of the method for the analysisof social mobility tables from the General Social Survey. |