7. Functional estimation in discrete-time processes
In applications, observed variables are often correlated. For example, X 1 ,..., X n may represent the rate of the euro against the dollar on days 1,..., n or the temperature observed in Paris on those same days at midday. We then say that the sequence (X n , n ≥ 1) is a discrete-time process.
If the X i have the same unknown density distribution f, kernel or projection density estimators can be constructed. These estimators will remain effective if the process has properties of "asymptotic independence", in other words X m and X n are "almost" independent when
Exclusive to subscribers. 97% yet to be discovered!
You do not have access to this resource.
Click here to request your free trial access!
Already subscribed? Log in!
The Ultimate Scientific and Technical Reference
This article is included in
Mathematics
This offer includes:
Knowledge Base
Updated and enriched with articles validated by our scientific committees
Services
A set of exclusive tools to complement the resources
Practical Path
Operational and didactic, to guarantee the acquisition of transversal skills
Doc & Quiz
Interactive articles with quizzes, for constructive reading
Functional estimation in discrete-time processes
Bibliography
Exclusive to subscribers. 97% yet to be discovered!
You do not have access to this resource.
Click here to request your free trial access!
Already subscribed? Log in!
The Ultimate Scientific and Technical Reference