Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/202303 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
JRC Working Papers in Economics and Finance No. 2018/11
Verlag: 
Publications Office of the European Union, Luxembourg
Zusammenfassung: 
We point out that the simple slice sampler generates chains that are correlation-free when the target distribution is centrally symmetric. This property explains several results in the literature about the relative performance of the simple and product slice samplers. We exploit it to improve two algorithms often used to circumvent the slice inversion problem, namely stepping out and multivariate sampling with hyperrectangles. In the general asymmetric case, we argue that symmetrizing the target distribution before simulating greatly enhances the efficiency of the simple slice sampler. To achieve symmetry we focus on the Box-Cox transformation with parameters chosen to minimize a measure of skewness. This strategy is illustrated with several sampling problems.
Schlagwörter: 
Box-Cox transformation
Markov Chain Monte Carlo
multivariate sampling
JEL: 
C11
C15
Persistent Identifier der Erstveröffentlichung: 
ISBN: 
978-92-79-93405-6
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
485.58 kB





Publikationen in EconStor sind urheberrechtlich geschützt.