function [para,standard_deviation,fv]=my_mle(fun,para0,y)
[para,fv]=fminsearch(fun,para0,[],2,y);
n=length(para);
d=numericalfirstderivative(fun,para,1,y);
standard_deviation=sqrt(diag(pinv(d'*d)));
function f=numericalfirstderivative(fun,parameter,varargin)
n=length(parameter);
for i=1:n
a=zeros(n,1);
a(i)=min(parameter(i)*1e-6,1e-4);
y1(:,i)=feval(fun,parameter+a,varargin{:});
y2(:,i)=feval(fun,parameter-a,varargin{:});
f(:,i)=(y1(:,i)-y2(:,i))/2/a(i);
end
function f=mle(parameter,num,observations)
alpha=parameter(1);
beta=parameter(2);
lambda=parameter(3);
y=beta/alpha*((observations-lambda)/alpha).^(beta-1)*exp((-(observations-lambda)/alpha).^beta)
if num==1
f=log(y);
else
f=-sum(log(y));
end
function f=mle(parameter,num,observations)
alpha=parameter(1);
beta=parameter(2);
lambda=parameter(3);
y=beta/alpha*((observations-lambda)/alpha).^(beta-1).*exp((-(observations-lambda)/alpha).^beta)
if num==1
f=log(y);
else
f=-sum(log(y));
end
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