Estimation for Hidden Processes 1.0

This package called EstimHidden is devoted to the non parametric estimation using model selection procedures of1/ the density of X in a convolution model where Z=X+noise1 is observed2/ 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)*noise2in 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.mAuthors : F. COMTE and Y. ROZENHOLCFor more information, see the following references:DENSITY DECONVOLUTION%%%%%%%%%%%%%%%%%%%1/ "Penalized contrast estimator for density deconvolution", The Canadian Journal of Statistics, 34, 431-452, (2006) b

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Authors,  Deconvestimatem,  Exampledensitym,  Exampleregressionm,  Examples,  Files,  Function,  Gaussian,  Logchi
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