威布尔分布参数估计.rar

clc clear %导入数据 r=xlsread('D:\matlab\bin\emd+神经网络\raodong.xlsx'); Raodong=bootrsp(r,20); raodong=Raodong(:); n=numel(raodong); %画概率密度分布图 vmin=min(raodong); vmax=max(raodong); l=linspace(vmin,vmax,25); [fre,R]=hist(raodong,l);%计算各区间个数 fe=cumsum(fre)/n;%计算累积频率； f1=fre/sum(fre); figure(1) bar(R,fe);%累计频率直方图 figure bar(R,f1) hold on plot(R,f1); hold off dx=(vmax-vmin)/24; f=gradient(fe,dx);%根据累计分布函数求导得概率密度 x=vmin:0.01:vmax; y=normpdf(x,mean(raodong),std(raodong)); x1=sort(raodong); [mu1, sigma1] = normfit(raodong); w1=normpdf(raodong,mu1,sigma1);%拟合均值为mean(A)，方差为std(A)的正态分布 figure(2) bar(R,f,'y'); hold on plot(R,f,'r'); % hold on % plot(x,y,'k'); legend('扰动项的频率分布直方图','扰动项的估计分布','正态分布'); hold off figure(3); hname={'normal' 'epanechnikov' 'box' 'triangle'}; colors={'r-','b-','k-','m-'}; for j=1:4 [f1,xi,u]=ksdensity(raodong,'function','pdf','kernel',hname{j}); plot(xi,f1,colors{j},'linewidth',2); hold on; end legend(hname{:}); [f1,xi,u]=ksdensity(raodong,'function','pdf','kernel','box'); % hold off figure; bar(R,f,'y'); hold on; plot(xi,f1,colors{j},'linewidth',2); % plot([a1,a1]) % hist(raodong); % m=ksdensity(xi,a1,'function','pdf'); %求解上下分位数 a1=prctile(xi,10); a2=prctile(xi,90); a3=prctile(xi,50);