5 citations · 17 across the 6 of their papers we have counts for
7 papers
Small area learning
Katarzyna Reluga, Nicola Salvati, Setareh Ranjbar +2
Small area estimation (SAE) traditionally borrows strength across domains through a prespecified working model. We introduce small area learning (SAL), a learner-agnostic framework…
Unified unconditional regression for multivariate quantiles, M-quantiles and expectiles
Luca Merlo, Lea Petrella, Nicola Salvati +1
In this paper, we develop a unified regression approach to model unconditional quantiles, M-quantiles and expectiles of multivariate dependent variables exploiting the multidimensi…
Quantile Mixed Hidden Markov Models for multivariate longitudinal data
Luca Merlo, Lea Petrella, Nikos Tzavidis
The identification of factors associated with mental and behavioral disorders in early childhood is critical both for psychopathology research and the support of primary health car…
Scale estimation and data-driven tuning constant selection for M-quantile regression
James Dawber, Nicola Salvati, Timo Schmid +1
M-quantile regression is a general form of quantile-like regression which usually utilises the Huber influence function and corresponding tuning constant. Estimation requires a nui…
Robust estimation for small domains in business surveys
Paul A. Smith, Chiara Bocci, Nikos Tzavidis +2
Small area (or small domain) estimation is still rarely applied in business statistics, because of challenges arising from the skewness and variability of variables such as turnove…
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…
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…