4 citations · 8 across the 7 of their papers we have counts for
8 papers
lqmix: an R package for longitudinal data analysis via linear quantile mixtures
Marco Alfó, Maria Francesca Marino, Maria Giovanna Ranalli +1
The analysis of longitudinal data gives the chance to observe how unit behaviors change over time, but it also poses a series of issues. These have been the focus of an extensive l…
Multiple imputation and selection of ordinal level 2 predictors in multilevel models. An analysis of the relationship between student ratings and teacher beliefs and practices
Leonardo Grilli, Maria Francesca Marino, Omar Paccagnella +1
The paper is motivated by the analysis of the relationship between ratings and teacher practices and beliefs, which are measured via a set of binary and ordinal items collected by…
A non-homogeneous hidden Markov model for partially observed longitudinal responses
Maria Francesca Marino, Marco Alfo'
Dropout represents a typical issue to be addressed when dealing with longitudinal studies. If the mechanism leading to missing information is non-ignorable, inference based on the…
A bi-dimensional finite mixture model for longitudinal data subject to dropout
Alessandra Spagnoli, Maria Francesca Marino, Marco Alfò
In longitudinal studies, subjects may be lost to follow-up, or miss some of the planned visits, leading to incomplete response sequences. When the probability of non-response, cond…
Semi-Parametric Empirical Best Prediction for small area estimation of unemployment indicators
Maria Francesca Marino, Maria Giovanna Ranalli, Nicola Salvati +1
The Italian National Institute for Statistics regularly provides estimates of unemployment indicators using data from the Labor Force Survey. However, direct estimates of unemploym…
M-quantile regression for multivariate longitudinal data: analysis of the Millennium Cohort Study data
Marco Alfo', Maria Francesca Marino, Maria Giovanna Ranalli +2
We propose a M-quantile regression model for the analysis of multivariate, continuous, longitudinal data. M-quantile regression represents an appealing alternative to standard regr…
Latent drop-out transitions in quantile regression
Maria Francesca Marino, Marco Alfó
Longitudinal data are characterized by the dependence between observations coming from the same individual. In a regression perspective, such a dependence can be usefully ascribed…
Quantile regression for longitudinal data: unobserved heterogeneity and informative missingness
Maria Francesca Marino, Nikos Tzavidis, Marco Alfo'
Linear quantile regression models aim at providing a detailed and robust picture of the (conditional) response distribution as function of a set of observed covariates. Longitudina…