bootstrap.zip_bootstrap_bootstrap matlab_bootstrap抽样_bootstrap重抽

function[H]=boottestvs(x,statfun,vzero,type,alpha,B1,B2,B3,varargin) % D=boottestvs(x,statfun,v_0,type,alpha,B1,B2,B3,PAR1,...) % % Hypothesis test for a characteristic (parameter) 'v' % of an unknown distribution based on the bootstrap % resampling procedure and variance stabilisation (VS). % % Inputs: % x - input vector data % statfun - the estimator of the parameter given as a Matlab function % v_0 - the value of vartheta under the null hypothesis % type - the type of hypothesis test. % % For type=1: H: v=v_0 against K: v~=v_0 % (two-sided hypothesis test) % For type=2: H: v<=v_0 against K: v>v_0 % (one-sided hypothesis test) % For type=3: H: v>=v_0 against K: v<v_0 % (one-sided hypothesis test) % (default type=1) % alpha - determines the level of the test % (default alpha=0.05) % B1 - numbers of bootstrap resamplings for VS % (default B1=100) % B2 - numbers of bootstrap resamplings for VS % (default B2=25) % B3 - number of bootstrap resamplings % (default B3=99) % PAR1,... - other parameters than x to be passed to statfun % % Outputs: % D - The output of the test. % D=0: retain the null hypothesis % D=1: reject the null hypothesis % % Example: % % D = boottestvs(randn(10,1),'mean',0); % Created by A. M. Zoubir and D. R. Iskander % May 1998 % % References: % % Efron, B.and Tibshirani, R. An Introduction to the Bootstrap. % Chapman and Hall, 1993. % % Tibshirani, R. Variance Stabilisation and the Bootstrap. % Biometrika, Vol.75, pp. 433-444, (1988). % % Zoubir, A.M. Bootstrap: Theory and Applications. Proceedings % of the SPIE 1993 Conference on Advanced Signal % Processing Algorithms, Architectures and Imple- % mentations. pp. 216-235, San Diego, July 1993. % % Zoubir, A.M. and Boashash, B. The Bootstrap and Its Application % in Signal Processing. IEEE Signal Processing Magazine, % Vol. 15, No. 1, pp. 55-76, 1998. pstring=varargin; if (exist('B3')~=1), B3=99; end; if (exist('B2')~=1), B2=25; end; if (exist('B1')~=1), B1=100; end; if (exist('alpha')~=1), alpha=0.05; end; if (exist('type')~=1), type=1; end; if (exist('vzero')~=1), error('Proivde the value of the paramter under the null hypothesis'); end; x=x(:); vhat=feval(statfun,x,pstring{:}); [vhatstar,ind]=bootstrp(B1,statfun,x,pstring{:}); bstats=bootstrp(B2,statfun,x(ind),pstring{:}); sigmastar2=var(bstats); [statsort,sigmasort,sigmasm2]=smooth(vhatstar',sigmastar2,B1/200); a=statsort; b=sigmasm2.^(-1/2); h=zeros(1,B1); h(1)=0; for i=2:B1, h(i)=h(i-1)+(a(i)-a(i-1))*(b(i)+b(i-1))/2; end; [vhatstar1,ind1]=bootstrp(B3,statfun,x,pstring{:}); ind=find(vhatstar1>=a(1) & vhatstar1<=a(B1)); ind1=find(vhatstar1<a(1)); ind2=find(vhatstar1>a(B1)); newv=vhatstar1(ind); newvs=vhatstar1(ind1); newvl=vhatstar1(ind2); hvec(ind)=interp1(a,h,newv)'; hvec(ind1)=(h(2)-h(1))/(a(2)-a(1))*(newvs-a(1))+h(1); hvec(ind2)=(h(B1)-h(B1-1))/(a(B1)-a(B1-1))*(newvl-a(B1-1))+h(B1-1); p=find(a>vhat); if isempty(p) hvhat=(h(B1)-h(B1-1))/(a(B1)-a(B1-1))*(vhat-a(B1-1))+h(B1-1); elseif p(1)==1, hvhat=(h(2)-h(1))/(a(2)-a(1))*(vhat-a(1))+h(1); else hvhat=interp1(a,h,vhat); end; p=find(a>vzero); if isempty(p) hvzero=(h(B1)-h(B1-1))/(a(B1)-a(B1-1))*(vzero-a(B1-1))+h(B1-1); elseif p(1)==1, hvzero=(h(2)-h(1))/(a(2)-a(1))*(vzero-a(1))+h(1); else hvzero=interp1(a,h,vzero); end; M=(B3+1)*(1-alpha); if type==1, Tstar=abs(hvec-hvhat); T=abs(hvhat-hvzero); ST=sort(Tstar); if T>ST(M), H=1; else H=0; end; elseif type==2, Tstar=(hvec-hvhat); T=(hvhat-hvzero); ST=sort(Tstar); if T>ST(M), H=1; else H=0; end; elseif type==3, Tstar=(hvec-hvhat); T=(hvhat-hvzero); ST=sort(Tstar); if T<ST(M), H=1; else H=0; end; end;