Displaying similar documents to “Filtering and parameter estimation for a jump stochastic process with discrete observations.”

A class of unbiased kernel estimates of a probability density function

Tomasz Rychlik (1995)

Applicationes Mathematicae


We propose a class of unbiased and strongly consistent nonparametric kernel estimates of a probability density function, based on a random choice of the sample size and the kernel function. The expected sample size can be arbitrarily small and mild conditions on the local behavior of the density function are imposed.

Particle filter with adaptive sample size

Ondřej Straka, Miroslav Šimandl (2011)



The paper deals with the particle filter in state estimation of a discrete-time nonlinear non-Gaussian system. The goal of the paper is to design a sample size adaptation technique to guarantee a quality of a filtering estimate produced by the particle filter which is an approximation of the true filtering estimate. The quality is given by a difference between the approximate filtering estimate and the true filtering estimate. The estimate may be a point estimate or a probability density...