Maximum likelihood estimation of SDEs is dicult because in general the transition density function of these processes is not known in closed form, and has to be approximated some- how. An approximation based on Ecient Importance Sampling (EIS) is detailed. Monte Carlo experiments, based on widely used diusion processes, evaluate its performance against an al- ternative IS strategy, showing that EIS is at least equivalent, if not superior, while allowing a greater exibility needed when examining more complicated models.

Efficient Importance Sampling Maximum Likelihood Estimation of Stochastic Differential Equations

ROSSI, EDUARDO
2004-01-01

Abstract

Maximum likelihood estimation of SDEs is dicult because in general the transition density function of these processes is not known in closed form, and has to be approximated some- how. An approximation based on Ecient Importance Sampling (EIS) is detailed. Monte Carlo experiments, based on widely used diusion processes, evaluate its performance against an al- ternative IS strategy, showing that EIS is at least equivalent, if not superior, while allowing a greater exibility needed when examining more complicated models.
2004
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/130741
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact