A semiparametric finite mixture of regression models is defined, with concomitant information assumed to influence both the component weights and the conditional means. The contribution of a concomitant variable is flexibly specified as a smooth function represented by cubic splines. A Bayesian estimation procedure is proposed and an empirical analysis of the baseball salaries dataset is illustrated.
Marco Berrettini, Giuliano Galimberti, Saverio Ranciati (2021). Semiparametric finite mixture of regression models with Bayesian P-splines. Firenze : Firenze University Press.
Semiparametric finite mixture of regression models with Bayesian P-splines
Marco Berrettini;Giuliano Galimberti;Saverio Ranciati
2021
Abstract
A semiparametric finite mixture of regression models is defined, with concomitant information assumed to influence both the component weights and the conditional means. The contribution of a concomitant variable is flexibly specified as a smooth function represented by cubic splines. A Bayesian estimation procedure is proposed and an empirical analysis of the baseball salaries dataset is illustrated.File in questo prodotto:
Eventuali allegati, non sono esposti
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
