In this paper we explore the use of higherorder tail area approximations for Bayesian simulation. These approximations give rise to alternative simulation schemes to MCMC for Bayesian computation of marginal posterior distributions for a scalar parameter of interest, in the presence of nuisance parameters. Their advantage over MCMC methods is that samples are drawn independently and much lower computational time is needed. The methods are illustrated by a genetic linkage model, a normal regression with censored data and a logistic regression model.
Marginal posterior simulation via higher-order tail area approximations
Ruli, Erlis;Ventura, Laura;Sartori, Nicola
2012
Abstract
In this paper we explore the use of higherorder tail area approximations for Bayesian simulation. These approximations give rise to alternative simulation schemes to MCMC for Bayesian computation of marginal posterior distributions for a scalar parameter of interest, in the presence of nuisance parameters. Their advantage over MCMC methods is that samples are drawn independently and much lower computational time is needed. The methods are illustrated by a genetic linkage model, a normal regression with censored data and a logistic regression model.File in questo prodotto:
File | Dimensione | Formato | |
---|---|---|---|
euclid.ba.1393251773.pdf
accesso aperto
Licenza:
Non specificato
Dimensione
237.31 kB
Formato
Adobe PDF
|
237.31 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.