DeconvPen(Wname,s2,Dtab,n)

function pen = DeconvPen(Wname,s2,Dtab,n)

% Wname : type of the known noise
% s2 : variance of the known noise
% Dtab : arry of model "dimensions"

cste = 1; % 3; % max([2.5*min([(1-1/s2n)^2 1]) 0.6]);
c1 = 1/(1+s2); % 8
c2 = 3;
c3L = 3; % 2
c3N = 3; % 18;
c3C = 3;
c4 = 12; % 6*(1+1/s2n)^2;

p2s2 = pi^2*s2;
Ltab = pi*Dtab; 

a = 1/4/(pi-2);
% Mtab = pi*(Ltab<4)+a*(Ltab-2).^2.*(2<=Ltab&Ltab<4)+Ltab.*(Ltab>=4);
% Mtab = max(Ltab,pi);
Mtab = Ltab;

pen = zeros(size(Dtab));
switch(lower(Wname))
    case 'logchi2'
        U = linspace(0,1,1000); dU = diff(U);
        for j=1:length(Dtab)
            L = Ltab(j); M = Mtab(j);
            YU = 1./abs(FTlogchi2(L*U,s2)).^2; I=IntByTrapz(dU,YU);
            pen(j) = (L+c1*log(M)^c2+c3C*L^2/3)*I;
            % pen(j) = 0.5*(L+c1*log(M)^c2+c3C*L^2/3)*I;
        end,
	case {'symexp'}
        pen = Ltab+c1*log(Mtab).^c2+c3L*Ltab.^3*s2/3+c4*Ltab.^5*s2^2/20;
%        pen = pen/pi;
	case {'gaussian','normal'}, 
        U = linspace(0,1,1000); dU = diff(U);
        for j=1:length(Dtab)
            L = Ltab(j); M = Mtab(j);
            YU = exp((L*U).^2*(0*exp(-sqrt(n)*s2)+s2)); I=IntByTrapz(dU,YU);
            pen(j) = (L+c1*log(M)^c2+c3N*L^3*s2/3)*I;
        end,
    otherwise
        disp('unknown noise type')
end;

pen = cste*pen;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This is part of the package EstimHidden devoted to the estimation 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.
%    by F. COMTE, Y. ROZENHOLC, and M.-L. TAUPIN
%
% 2/ "Finite sample  penalization in adaptive density deconvolution", 
%    Journal of Statistical Computation and Simulation. 
%    Available online.
%    by F. COMTE, Y. ROZENHOLC, and M.-L. TAUPIN
%
% 3/ "Adaptive density estimation for general ARCH models", 
%    Preprint HAL-CNRS : hal-00101417  at http://hal.archives-ouvertes.fr/
%    by F. COMTE, J. DEDECKER, and  M.-L. TAUPIN. 
%
% REGRESSION and AUTO-REGRESSION
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% 4/ "Nonparametric estimation of the regression function in an
%    errors-in-variables model", 
%    Statistica Sinica, 17, n3, 1065-1090, 2007. 
%    by F. COMTE and M.-L. TAUPIN
%
% 5/ "Adaptive estimation of the dynamics of a discrete time stochastic
%    volatility model", 
%    Preprint HAL-CNRS : hal-00170740 at http://hal.archives-ouvertes.fr/
%    by F. COMTE, C. LACOUR, and Y. ROZENHOLC. 
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% You can use this software for NON-COMMERCIAL USE ONLY. 
%
% You can distribute this sofware unchanged and only unchanged, which implies
% including all files found in the folder cointainning this file.
%
% This software, and any part of it, is proposed for NON-COMMERCIAL USE 
% ONLY. 
%
% Please, contact the author for and before any non-academic use
% of this software.
%
% To reproduce this code or any part of this code in the original language 
% or in any other language, for commercial use, please contact the Author
%
% For academic purpose, cite this package and the connected papers.
%
% Corresponding author : Y. Rozenholc, [e-mail]
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Examples in files ExampleDensity.m and ExampleRegression.m
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%