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Roberto Colombi

Publications and source records attributed to Roberto Colombi.

3 recordsLinked to original sources

Modelling Ordinal Responses with Uncertainty: a Hierarchical Marginal Model with Latent Uncertainty components

In responding to rating questions, an individual may give answers either according to his/her knowledge/awareness or to his/her level of indecision/uncertainty, typically driven by a response style. As ignoring this dual behaviour may lead to misleading results, we define a multivariate model for ordinal rating responses, by introducing, for every item, a binary latent variable that discriminates aware from uncertain responses. Some independence assumptions among latent and observable variables characterize the uncertain behaviour and make the model easier to interpret. Uncertain responses are modelled by specifying probability distributions that can depict different response styles characterizing the uncertain raters. A marginal parametrization allows a simple and direct interpretation of the parameters in terms of association among aware responses and their dependence on explanatory factors. The effectiveness of the proposed model is attested through an application to real data and supported by a Monte Carlo study.

stat.ME

Testing order restrictions in contingency tables

Several interesting models for contingency tables are defined by a system of equality and inequality constraints on a suitable set of marginal log-linear parameters. After reviewing the most common difficulties which are intrinsic to order restricted testing problems, we propose two new families of testing procedures, based on similar attempts appeared in the econometric literature, in order to increase the probability of detecting several relevant violations of the supposed order relations. One set of procedures is based on the decomposition of the log-likelihood ratio when testing the given set of inequalities and the nested model derived by forcing inequalities into strict equalities. The other set uses the asymptotic joint normal distribution of the estimates of the marginal log-linear parameters to be constrained.

math.ST

Multiple Hidden Markov Models for Categorical Time Series

We introduce multiple hidden Markov models (MHMMs) where an observed multivariate categorical time series depends on an unobservable multivariate Mar- kov chain. MHMMs provide an elegant framework for specifying various independence relationships between multiple discrete time processes. These independencies are interpreted as Markov properties of a mixed graph and a chain graph associated to the latent and observable components of the MHMM, respectively. These Markov properties are also translated into zero restrictions on the parameters of marginal models for the transition probabilities and the distributions of the observable variables given the latent states.

stat.ME