帮忙看看matlab极大似然估计程序哪出错了

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[para,std_dev]=my_mle('gh_log_fun',[-1;25;1;0.0005;0.02],y)
??? Attempt to execute SCRIPT gh_log_fun as a function:
E:\软件包\matlab\bin\gh_log_fun.m

Error in ==> fminsearch at 205
fv(:,1) = funfcn(x,varargin{:});

Error in ==> my_mle at 25
[para,fv]=fminsearch(fun,para0,[],2,varargin{:});

function[para,standard_deviation,fv]=my_mle(fun,para0,varargin)%estimate parameters and standard errors when using maximum likelihood%estimation(MLE)%input%fun:a function defined by users for calculating log probability density%function(pdf) and negative sum of logatithm of pdf%para0:given initial parameters%varargin;other needed inputs required by fun%output%para:estimated parameters%standard_deviation:standard deviations of estimated parameters%fv:maximized likelihood function value%%%%%%%%%%%%%%%%%%%%%%example1:estima mean and standard devition by realizations of a random%variable which is normally distributed%function f=mynormpdfsum(x,num,y)%yy=1/sqrt(2*pi)/x(2)*exp(-(y-x(1)).^2/2/x(2)^2);%if num==1%%(note: it must set to 1)%f=log(yy);%else f=-sum(log(yy));end%%%%%%%%%%%%%%y=2+3*randn(5000,1);%[para,standard_deviation]=my_mle('mynormpdfsum',[0;2],y)para0=para0(:);[para,fv]=fminsearch(fun,para0,[],2,varargin{:});fv=-fv;d=my_numerical_derivative(fun,para,1,varargin{:});standard_deviation=sqrt(diag(pinv(d'*d)));

function f=my_numerical_derivative(fun,parameter,varargin)%input%fun:the name of a function%parameter:given parameter with respect to which first-order derivative%is calculated%varargin:other needed inputs reguired by fun%output%f:numerical first order derivative of fun at parametern=length(parameter);for i =1:n    a=zeros(n,1);    a(i)=max(parameter(i)*le-6,le-6);    y1(;,i)=feval(fun,parameter+a,varargin{:});    y2(:,i)=feval(fun,parameter-a,varargin{:});    f(:,i)=(y1(:,i)-y2(:,i)/2/a(i));end

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关键词：MATLAB 极大似然估计 matla atlab 极大似然 matlab 程序

1. function[para,standard_deviation,fv]=my_mle(fun,para0,varargin)
   
2. %estimate parameters and standard errors when using maximum likelihood
   
3. %estimation(MLE)
   
4. %input
   
5. %fun:a function defined by users for calculating log probability density
   
6. %function(pdf) and negative sum of logatithm of pdf
   
7. %para0:given initial parameters
   
8. %varargin;other needed inputs required by fun
   
9. %output
   
10. %para:estimated parameters
    
11. %standard_deviation:standard deviations of estimated parameters
    
12. %fv:maximized likelihood function value
    
13. %%%%%%%%%%%%%%%%%%%%%
    
14. %example1:estima mean and standard devition by realizations of a random
    
15. %variable which is normally distributed
    
16. %function f=mynormpdfsum(x,num,y)
    
17. %yy=1/sqrt(2*pi)/x(2)*exp(-(y-x(1)).^2/2/x(2)^2);
    
18. %if num==1%%(note: it must set to 1)
    
19. %f=log(yy);
    
20. %else f=-sum(log(yy));end
    
21. %%%%%%%%%%%%%
    
22. %y=2+3*randn(5000,1);
    
23. %[para,standard_deviation]=my_mle('mynormpdfsum',[0;2],y)
    
24. para0=para0(:);
    
25. [para,fv]=fminsearch(fun,para0,[],2,varargin{:});
    
26. fv=-fv;
    
27. d=my_numerical_derivative(fun,para,1,varargin{:});
    
28. standard_deviation=sqrt(diag(pinv(d'*d)));

复制代码

1. function f=my_numerical_derivative(fun,parameter,varargin)
   
2. %input
   
3. %fun:the name of a function
   
4. %parameter:given parameter with respect to which first-order derivative
   
5. %is calculated
   
6. %varargin:other needed inputs reguired by fun
   
7. %output
   
8. %f:numerical first order derivative of fun at parameter
   
9. n=length(parameter);
   
10. for i =1:n
    
11.     a=zeros(n,1);
    
12.     a(i)=max(parameter(i)*le-6,le-6);
    
13.     y1(;,i)=feval(fun,parameter+a,varargin{:});
    
14.     y2(:,i)=feval(fun,parameter-a,varargin{:});
    
15.     f(:,i)=(y1(:,i)-y2(:,i)/2/a(i));
    
16. end

复制代码

帮忙给顶起来的

写得真好                                                                        
                                       
                                                     
                                                     
                                             
                                                                 
                                                                                 

这个代码里其中那个 le写错了，应该是1e 模型就对了