Simulated maximum likelihood estimation in transition models
In this paper we analyse the problem of modelling individual transitions in the presence of an incomplete sampling scheme. This problem is particularly cumbersome when a continuous time-scale is used for the modelling and when the model incorporates unobserved heterogeneity. This problem arises, for instance, when the observation is made at fixed time points or stops on an interval of time. In order to take this phenomenon into account, we propose to maximize the simulated likelihood using an importance function. The method can be applied to general continuous-time discrete-state-space processes and a broad class of incomplete sampling schemes.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Volume (Year): 1 (1998)
Issue (Month): ConferenceIssue ()
|Contact details of provider:|| Postal: |
Phone: +44 1334 462479
Web page: http://www.res.org.uk/
More information through EDIRC
|Order Information:||Web: http://www.ectj.org|
When requesting a correction, please mention this item's handle: RePEc:ect:emjrnl:v:1:y:1998:i:conferenceissue:p:c129-c153. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If references are entirely missing, you can add them using this form.