Nonparametric Estimation for some Specific Classes of Hidden Markov Models.
Abstract
We study the problem of nonparametric estimation of the stationary and transition densities of a regular Markov chain based on noisy observations when the density of the noise's terms k(x) has a Fourier transform with decay of order |w|−α as w → ∞. Adopting the formalism of the wavelet-vaguelette decomposition (WVD), we propose estimation procedures based on thresholding of the WVD coefficients which are shown to be nearly optimal over a wide range of Besov classes for the variety of global Lp0 error measures, 1 6 p0 < ∞.
Origin : Files produced by the author(s)
Loading...