Estimation for Hidden Processes

Nonparametric estimation of density, regression or variance functions for hidden processes using mod

This package called EstimHidden is devoted to the non parametric estimation using model selection procedures of

1/ the density of X in a convolution model where Z=X+noise1 is observed

2/ the functions b (drift) and s^2 (volatility) in an "errors in variables" model where Z and Y are observed and assumed to follow:
Z=X+noise1 and Y=b(X)+s(X)*noise2.

3/ the functions b (drift) and s^2 (volatility) in an stochastic volatility model where Z is observed and follows:
Z=X+noise1 and X_{i+1} = b(X_i) + s(X_i)*noise2

in any cases the density of noise1 is known. We consider three cases for this density : Gaussian ('normal'), Laplace ('symexp') and log(Chi2) ('logchi2)

See function DeconvEstimate.m and examples in files ExampleDensity.m and ExampleRegression.m

Authors : F. COMTE and Y. ROZENHOLC

For more information, see the following references:

DENSITY DECONVOLUTION
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1/ "Penalized contrast estimator for density deconvolution", The Canadian Journal of Statistics, 34, 431-452, (2006) b