Handling non-ignorable dropouts in longitudinal data: A conditional model based on a latent Markov heterogeneity structure

We illustrate a class of conditional models for the analysis of longitudinal data suffering attrition in random effects models framework, where the subject-specific random effects are assumed to be discrete and to follow a time-dependent latent process. The latent process accounts for unobserved heterogeneity and correlation between individuals in a dynamic fashion, and for dependence between the observed process and the missing data mechanism. Of particular interest is the case where the missing mechanism is non-ignorable. To deal with the topic we introduce a conditional to dropout model. A shape change in the random effects distribution is considered by directly modeling the effect of the missing data process on the evolution of the latent structure. To estimate the resulting model, we rely on the conditional maximum likelihood approach and for this aim we outline an EM algorithm. The proposal is illustrated via simulations and then applied on a dataset concerning skin cancers. Comparisons with other well-established methods are provided as well

FAIRNESS OF NATIONAL HEALTH SERVICE IN ITALY: A BIVARIATE CORRELATED RANDOM EFFECTS MODEL

In this paper we consider a possible way of measuring equity in health as the absence of systematic disparities in health (or in the major social determinants of health) between groups with different levels of underlying social advantage/disadvantage. Starting from the fairness approach developed by the World Health Organization, we propose to extend the analysis of fairness in nancing contribution through a generalized linear mixed models framework by introducing a bivariate correlated random effects model. We aim at analyzing the burden of health care payment on Italian households by modeling catastrophic payments and impoverishment due to health care expenditures. For this purpose, we describe a bivariate model for binary data, where association between the outcomes is modeled through outcome-specic latent effects which are assumed to be correlated; we show how model parameters can be estimated in a nite mixture context. By using such model specication, the fairness of the Italian national health service is investigated.fairness, health care, random eects models, binary data, non parametric maximum likelihood.

Students' evaluation of academic courses: An exploratory analysis to an Italian case study

Students' evaluations of teaching is a common practice in higher education institutions, with the main purpose of improving course quality and effectiveness. In this paper we would like to contribute to the existing literature on course and teaching evaluation by providing an empirical analysis based on questionnaires collected by an Italian private institution, namely the Libera Università Maria Ss. Assunta (LUMSA), for several degrees in Social Sciences. In order to identify the main factors affecting students' satisfaction, we use not only teaching indicators and degree-specific characteristics, but also control for student-specific characteristics. Our analysis is based on a Multiple Correspondence Analysis for categorical variables, which represents a very useful method to study the multidimensional relationship among qualitative variables, along with a hierarchical clustering, in order to better summarize the results. Our findings reveal that student satisfaction relates to teaching and course organization. Moreover, we find some evidence that students typically evaluate their course on the basis of their experience rather than their personal interests. publishedVersio

ON BASELINE CONDITIONS FOR ZERO-INFLATED LONGITUDINAL COUNT DATA

We describe a mixed-effects hurdle model for zero-inflated longitudinal count data, where a baseline variable is included in the model specification. Association between the count data process and the endogenous baseline variable is modeled through a latent structure, assumed to be dependent across equations. We show how model parameters can be estimated in a fnite mixture context, allowing for overdispersion, multivariate association and endogeneity of the baseline variable. The model behavior is investigated through a large scale simulation experiment. An empirical example on health care utilization data is provided.Hurdle model - Baseline conditions - Longitudinal count data - Zero-inflation.

Students’ evaluation of academic courses: An exploratory analysis to an Italian case study

Students’ evaluations of teaching is a common practice in higher education institutions, with the main purpose of improving course quality and effectiveness. In this paper we would like to contribute to the existing literature on course and teaching evaluation by providing an empirical analysis based on questionnaires collected by an Italian private institution, namely the Libera Università Maria Ss. Assunta (LUMSA), for several degrees in Social Sciences. In order to identify the main factors affecting students’ satisfaction, we use not only teaching indicators and degree-specific characteristics, but also control for student-specific characteristics. Our analysis is based on a Multiple Correspondence Analysis for categorical variables, which represents a very useful method to study the multidimensional relationship among qualitative variables, along with a hierarchical clustering, in order to better summarize the results. Our findings reveal that student satisfaction relates to teaching and course organization. Moreover, we find some evidence that students typically evaluate their course on the basis of their experience rather than their personal interests.publishedVersio

A copula-based multivariate hidden Markov model for modelling momentum in football

We investigate the potential occurrence of change points - commonly referred to as "momentum shifts" - in the dynamics of football matches. For that purpose, we model minute-by-minute in-game statistics of Bundesliga matches using hidden Markov models (HMMs). To allow for within-state correlation of the variables considered, we formulate multivariate state-dependent distributions using copulas. For the Bundesliga data considered, we find that the fitted HMMs comprise states which can be interpreted as a team showing different levels of control over a match. Our modelling framework enables inference related to causes of momentum shifts and team tactics, which is of much interest to managers, bookmakers, and sports fans

A copula-based multivariate hidden Markov model for modelling momentum in football

We investigate the potential occurrence of change points—commonly referred to as “momentum shifts”—in the dynamics of football matches. For that purpose, we model minute-by-minute in-game statistics of Bundesliga matches using hidden Markov models (HMMs). To allow for within-state dependence of the variables, we formulate multivariate state-dependent distributions using copulas. For the Bundesliga data considered, we find that the fitted HMMs comprise states which can be interpreted as a team showing different levels of control over a match. Our modelling framework enables inference related to causes of momentum shifts and team tactics, which is of much interest to managers, bookmakers, and sports fans.publishedVersio