Suppose real valued data X1, X2, ..., Xn are from a mixture distribution f(x) = (f1(x) + f2(x))/2, the component distributions f1(x) and f2(x) meet the following conditions:
f1(x) = f2(-x);
f1(x) = f2(x)exp(x/A);
can we estimate A based on X1, X2, ..., Xn? Many thanks for any hints
Thanks for your discussion. I understand this. But what if f1(x) and f2(x) are other distributions? The identity may still holds but the ML estimator derived for the Laplace distribution may not work. Is there any way to derive without assuming the specific component distributions?