Stochastic kernel estimation
In statistics, a stochastic kernel estimate is an estimate of the stochastic kernel (the continuous analogue of the transition function[1]) of a (usually discrete-time) stochastic process. Often, this is an estimate of the conditional density function obtained using kernel density estimation. The estimated conditional distribution can then be used to derive estimates of other properties of the stochastic process, such as the stationary distribution.
External links
- Conditional Stochastic Kernel Estimation by Nonparametric Methods (Laurini, Márcio P. & Valls Pereira, Pedro L.)
References
- ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:10.1016/S0014-4983(02)00023-2, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with
|doi=10.1016/S0014-4983(02)00023-2instead.
 | This statistics-related article is a stub. You can help Wikipedia by expanding it. |