Functional estimation : Probability density estimation using the kernel method

Probability density estimation using the kernel method
Functional estimation

Author : Denis BOSQ

Publication date: October 10, 2009 | Lire en français

Add to my library

4. Probability density estimation using the kernel method

Density function estimation plays a central role in functional estimation; the problem is difficult and the applications are numerous.

The situation is as follows: we observe independent real random variables X ₁ ,..., X _n with the same law μ of unknown density f, so we have :

P (a \leq X_{i} \leq b) = μ ([a, b]) = \int_{a}^{b} f (x)

You do not have access to this resource.

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!

Ongoing reading
Probability density estimation using the kernel method

(1) - BERLINET (A.), DEVROYE (L) - A comparison of kernel density estimates - Publ. Inst. Statist. Univ. Paris, 38 (3), p. 3-59 (1994).
(2) - BLANKE (D.), PUMO (B.) - Optimal sampling for density estimation in continuous...

You do not have access to this resource.

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!

Article included in this offer

Updated and enriched with articles validated by our scientific committees

Services

A set of exclusive tools to complement the resources