Stochastic kernel estimation

In statistics, a stochastic kernel estimate is an estimate of the transition matrix 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

• Example using stochastic kernel for regression (Kardi Teknomo's tutorial)
• Conditional Stochastic Kernel Estimation by Nonparametric Methods (Laurini, Márcio P. & Valls Pereira, Pedro L.)