Estimation for Hidden Processes
No License
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
%%%%%%%%%%%%%%%%%%%
1/ "Penalized contrast estimator for density deconvolution", The Canadian Journal of Statistics, 34, 431-452, (2006) b
Cite As
Yves Rozenholc (2024). Estimation for Hidden Processes (https://www.mathworks.com/matlabcentral/fileexchange/16797-estimation-for-hidden-processes), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxCategories
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.
EstimHidden/
Version | Published | Release Notes | |
---|---|---|---|
1.0.0.0 |