Showing papers by "Francesco Bartolucci published in 2004"
TL;DR: It is shown, through a simulation study, that an effective strategy is fitting a regression model based on the assumption that the error terms follow a mixture of normal distributions, with greater precision of the parameter estimates and confidence intervals.
44 citations
TL;DR: An exact conditional approach is developed to test for certain forms of positive association between two ordinal variables, based on the use of a test statistic measuring the goodness-of-fit of the model formulated according to the type ofpositive association of interest.
19 citations
01 Jan 2004
TL;DR: A well-known method for estimating the size, N, of a certain population is the capture-recapture method, and this methodology was also applied in medical and social contexts where it is important to estimate the number of subjects with a certain disease or in a particular situation.
Abstract: A well-known method for estimating the size, N, of a certain population is the capture-recapture method (for a review see Yip et al., 1995a and Schwarz and Seber, 1999). The first motivations to the development of these methods arose in biology where researchers were interested in estimating the number of animals of a certain species (see, for instance, Schnabel, 1938, and Darroch, 1958). Subsequently, this methodology was also applied in medical and social contexts where it is important to estimate the number of subjects with a certain disease or in a particular situation (Yip et al., 1995b).
7 citations
TL;DR: In this article, a new methodology for modelling the joint distribution of ordered categorical variables with finite mixtures where hypotheses of interest may be expressed by linear equality and inequality constraints on the parameters is presented.
Abstract: We present a new methodology for modelling the joint distribution of ordered categorical variables with finite mixtures where hypotheses of interest may be expressed by linear equality and inequality constraints on the parameters. The connection with non-parametric polytomous item response theory models is outlined and an application to the quality of life of asthmatic patients is examined. An algorithm for constrained maximum likelihood estimation is described and an analysis of deviance table for hypotheses testing based on the asymptotic distribution of the likelihood ratio statistic is outlined.
7 citations
01 Jan 2004
TL;DR: In this paper, the authors proposed a class of estimators of the Bayes factor which is based on an extension of the bridge sampling identity of Meng & Wong (1996) and makes use of the output of the reversible jump algorithm of Green ( 1995).
Abstract: SUMMARY We propose a class of estimators of the Bayes factor which is based on an extension of the bridge sampling identity of Meng & Wong (1996) and makes use of the output of the reversible jump algorithm of Green ( 1995). Within this class we give the optimal estimator and also a suboptimal one which may be simply computed on the basis of the acceptance probabilities used within the reversible jump algorithm for jumping between models. The proposed estimators are very easily computed and lead to a substantial gain of efficiency in estimating the Bayes factor over the standard estimator based on the reversible jump output. This is illustrated through a series of Monte Carlo simulations involving a linear and a logistic regression model.
2 citations