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matlab - 使用 mle() 估计自定义分布的参数

转载 作者:太空宇宙 更新时间:2023-11-03 19:24:47 25 4
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我有以下代码,我希望估计自定义分布的参数。 For more details on the distribution .然后使用估计的参数,我想看看估计的 PDF 是否类似于给定数据的分布(它应该与给定数据的分布相匹配)。

[编辑]:“x”现在包含数据样本而不是 PDF

主要代码为:

x           = [0.0320000000000000   0.0280000000000000  0.0280000000000000  0.0270000000000000  0.0320000000000000  0.0320000000000000  0.0480000000000000  0.0890000000000000  0.0500000000000000  0.0620000000000000  0.0480000000000000  0.0300000000000000  0.0520000000000000  0.0460000000000000  0.0540000000000000  0.0520000000000000  0.0510000000000000  0.0310000000000000  0.0330000000000000  0.0330000000000000  0.0380000000000000  0.0850000000000000  0.102000000000000   0.0290000000000000  0.0530000000000000  0.0590000000000000  0.0320000000000000  0.0800000000000000  0.0410000000000000  0.0280000000000000  0.0670000000000000  0.0350000000000000  0.0420000000000000  0.0280000000000000  0.0370000000000000  0.0480000000000000  0.0330000000000000  0.101000000000000   0.0420000000000000  0.0840000000000000  0.0340000000000000  0.0900000000000000  0.0900000000000000  0.0460000000000000  0.0290000000000000  0.0330000000000000  0.0350000000000000  0.0330000000000000  0.0320000000000000  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0.0440000000000000  0.0290000000000000  0.0280000000000000  0.0350000000000000  0.0840000000000000  0.0660000000000000  0.0410000000000000  0.0300000000000000  0.0440000000000000  0.0450000000000000  0.0470000000000000  0.0620000000000000  0.0420000000000000  0.0300000000000000  0.0330000000000000  0.0320000000000000  0.0440000000000000  0.0700000000000000  0.0340000000000000  0.0420000000000000  0.0480000000000000  0.0360000000000000  0.0590000000000000  0.106000000000000   0.0280000000000000  0.0540000000000000  0.0870000000000000  0.0300000000000000  0.0300000000000000  0.0370000000000000  0.0210000000000000  0.0360000000000000  0.0910000000000000  0.126000000000000   0.0780000000000000  0.0510000000000000  0.0500000000000000  0.0370000000000000  0.0540000000000000  0.0380000000000000  0.0350000000000000  0.0480000000000000  0.0300000000000000  0.0340000000000000  0.133000000000000   0.0330000000000000  0.0340000000000000  0.0480000000000000  0.0590000000000000  0.0460000000000000  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0.0530000000000000];

Censored = ones(1,size(x,2));%
custpdf = @eval_custpdf;
custcdf = @eval_custcdf;
options = statset('Display','iter','MaxFunEvals',1000,'MaxIter',1000,...
'FunValCheck','off','TolX',1.0e-10,'TolFun',1.0e-10);
phat = mle(x,'pdf', custpdf,'cdf', custcdf,...
'start',[0.6,0.02,1.01,2,4,-10],...
'lowerbound',[0 0 0 0 0 -inf],...
'upperbound',[inf inf inf inf inf inf],...
'Censoring',Censored,...
'Options',options);;

% Checking how close the estimated PDF and CDF match with those from the data x
figure();
h = histogram(x,'Normalization','probability');hold on
x_times = h.BinEdges(1:end-1) + h.BinWidth/2 ;
y_vals = custpdf(x_times, phat(1), phat(2), phat(3), phat(4), phat(5), phat(6))./...
sum(custpdf(x_times, phat(1), phat(2), phat(3), phat(4), phat(5), phat(6)),'omitnan');
plot(x_times,y_vals,'linewidth',2)
legend('Data','Estimated PDF')

函数是:

function out = eval_custpdf(x,myalpha,mytheta,mybeta,a,b,c)
first_integral = integral(@(x) eval_K(x,a,b,c),0,1).^-1;
theta_t_ratio = (mytheta./x);
incomplete_gamma = igamma(myalpha,theta_t_ratio.^mybeta);
n_gamma = gamma(myalpha);
exponent_term = exp(-theta_t_ratio.^mybeta-(c.*(incomplete_gamma./n_gamma)));


numerator = first_integral.* mybeta.*incomplete_gamma.^(a-1).*...
theta_t_ratio.^(myalpha*mybeta+1).*exponent_term;
denominator = mytheta.* n_gamma.^(a+b-1).* (n_gamma-incomplete_gamma.^mybeta).^(1-b);

out = numerator./denominator;
end

function out = eval_custcdf(x,myalpha,mytheta,mybeta,a,b,c)
out = zeros(size(x));
for i = 1: length(x)
first_integral = integral(@(x) eval_K(x,a,b,c),0,1).^-1;
theta_t_ratio = mytheta./x(i);
incomplete_gamma = igamma(myalpha,theta_t_ratio.^mybeta);
n_gamma = gamma(myalpha);
second_integral = integral(@(x) eval_K(x,a,b,c),0,...
incomplete_gamma.^mybeta./n_gamma);
% second_integral = integral(@(x) eval_K(x,a,b,c),0,2);
out(i) = first_integral*second_integral;
end
end

function out = eval_K(x,a,b,c)

out = x.^(a-1).*(1-x).^(b-1).*exp(-c.*x);

end

但是,我一直没有成功获得想要的PDF。正如您在图中看到的,估计的 PDF(橙色线)没有追踪 'x'(蓝色条)的直方图。

[更新] 归一化直方图

enter image description here请注意,我改变了参数的初始值。但这是非常耗时的。我还增加了迭代次数并最小化了公差,但还没有成功。有没有比 mle 更好的方法来估计参数?

如有任何帮助,我们将不胜感激。

提前致谢。

最佳答案

从帮助中注意到这一点

If the 'censoring' name/value pair is not present, you may omit the 'cdf' name/value pair.

为我们提供了第一个调试建议。因此从输入列表中删除审查部分和 CDF 并运行

phat =  mle(x,'pdf', @eval_custpdf,'start',[0.6,0.02,1.01,2,4,-10]);
phat = mle(x,'pdf', @eval_custpdf,'start',phat); %Restart for better result

产量图

plot1

告诉我们问题可能出在 CDF 函数中。与问题中给出的链接相比,我们看到这一行

second_integral     =  integral(@(x) eval_K(x,a,b,c),0,incomplete_gamma.^mybeta./n_gamma);

应该是

second_integral =  integral(@(x) eval_K(x,a,b,c),0,incomplete_gamma./n_gamma);

关于matlab - 使用 mle() 估计自定义分布的参数,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/56625339/

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