We propose a multi-state quantile regression model that admits a cure-fraction for each possible transition, so that individuals may not experience that event. A discrete latent variable allows us to take into account unobserved heterogeneity. The model is estimated in a Bayesian framework, without specification of the number of latent classes. We are motivated by an original application to spells of imprisonment in the USA.
Barone, R., Farcomeni, A., Latent Class Multi-state Quantile Regression, in Methodological and Applied Statistics and Demography III, (Bari, 17-20 June 2024), Springer, Bari 2024: 133-138 [https://hdl.handle.net/10807/323988]
Latent Class Multi-state Quantile Regression
Barone, Rosario
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2024
Abstract
We propose a multi-state quantile regression model that admits a cure-fraction for each possible transition, so that individuals may not experience that event. A discrete latent variable allows us to take into account unobserved heterogeneity. The model is estimated in a Bayesian framework, without specification of the number of latent classes. We are motivated by an original application to spells of imprisonment in the USA.
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