Stochastic Differential Mixed-Effects Models (Articolo in rivista)

Type
Label
  • Stochastic Differential Mixed-Effects Models (Articolo in rivista) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1111/j.1467-9469.2009.00665.x (literal)
Alternative label
  • Picchini, U.; De Gaetano, A.; Ditlevsen, S. (2010)
    Stochastic Differential Mixed-Effects Models
    in Scandinavian journal of statistics
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Picchini, U.; De Gaetano, A.; Ditlevsen, S. (literal)
Pagina inizio
  • 67 (literal)
Pagina fine
  • 90 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9469.2009.00665.x/abstract (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 37 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 24 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • - (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Picchini, U.; De Gaetano, A.; Ditlevsen, S. (literal)
Titolo
  • Stochastic Differential Mixed-Effects Models (literal)
Abstract
  • Stochastic differential equations have been shown useful in describing random continuous time processes. Biomedical experiments often imply repeated measurements on a series of experimental units and differences between units can be represented by incorporating random effects into the model. When both system noise and random effects are considered, stochastic differential mixed-effects models ensue. This class of models enables the simultaneous representation of randomness in the dynamics of the phenomena being considered and variability between experimental units, thus providing a powerful modelling tool with immediate applications in biomedicine and pharmacokinetic/pharmacodynamic studies. In most cases the likelihood function is not available, and thus maximum likelihood estimation of the unknown parameters is not possible. Here we propose a computationally fast approximated maximum likelihood procedure for the estimation of the non-random parameters and the random effects. The method is evaluated on simulations from some famous diffusion processes and on real data sets. (literal)
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Autore CNR di
Prodotto
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
Insieme di parole chiave di
data.CNR.it