I The CDF from the characteristic function

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The discussion revolves around finding the cumulative distribution function (CDF) for a random variable defined as the sum of independent and identically distributed (i.i.d.) exponential random variables. The initial approach using the moment generating function (MGF) was abandoned due to undefined means, leading to the use of the characteristic function (CF) instead. The user encountered issues while attempting to evaluate integrals related to the CF, resulting in non-finite numerical outputs that contradict the expected properties of a CDF. There is confusion regarding the application of the Gil-Pelaez theorem for inverting the CF, with participants suggesting possible errors in the derivations and numerical evaluations. The conversation highlights the complexities of deriving distributions from CFs and the potential pitfalls in numerical integration methods.
  • #31
Look at post #27. \int_0^{\infty}\frac{sin(tx)}{t}dt=0,\ for\ x=0
 
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  • #32
Ooops! I forgot about that. My mistake. Thanks!

So, this is an indication that the derivation is correct, right? The only difference in my equations is that I use the formula ##\text{ci}(x)\pm j\,\text{si}(x) = \text{Ei}(\pm j\,x)##, where ##\text{Ei}(x) = -\int_{-x}^{\infty}\frac{e^{-t}}{t}\,dt = -E_1(-x)##. The question is: why the numerical integration doesn't evaluate as a CDF for the general case using MATLAB and Mathematica? Is it a precision issue in the software tools?
 
  • #33
I don't know how MATLAB or Mathematica were set up. There may be some limitations to their applicability. Since I have never used them I can't help here. When I need complicated integrals I look up the table I previously mentioned.
 
  • #34
mathman said:
I don't know how MATLAB or Mathematica were set up. There may be some limitations to their applicability. Since I have never used them I can't help here. When I need complicated integrals I look up the table I previously mentioned.

But theoretically, the derivation should be correct, right? Because I just raise the CF of a single X to the power K, and then solve for the CDF as we did for a single X.
 
  • #35
The basic theory (invert the nth power of the char. function gives the cdf of the sum of n terms) is correct. But as we have seen with the case n=1, the calculation can be very tricky. The Gil-Pelaez theorem had to be used and getting the imaginary part can be messy.
 
  • #36
Yes, but supposedly, the software tools take care of the imaginary part using their functions to extract the imaginary part. We don't have to concern ourselves with doing that manually.
 
  • #37
I can't comment on these tools, since I've never used them. If you are sure they should work, I suggest you check the input if the answer doesn't look right.
 

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