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Let $X\sim\chi_m^2$ and $Y\sim\chi_n^2$ be two independent variables. How to calculate or estimate the expectation of $\sqrt{aX+bY}$, where $a,b>0$?

kjetil b halvorsen
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1 Answers1

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$\chi_m^2$ variables are also known as $\Gamma(m/2, 2)$, using the shape-scale parameterization of the gamma distribution.

Scaled gamma distributions are themselves gamma, with the scale parameter scaled equally. So $a X \sim \Gamma(m / 2, 2 a)$, $b Y \sim \Gamma(n / 2, 2 b)$.

The sum of independent gammas with the same scale is itself gamma, so that if $b = a$, then $a X + a Y \sim \Gamma(\frac{n+m}{2}, 2 a)$. In that case, its square root is a Nakagami distribution, with mean $$\mathbb E \sqrt{a X + a Y} = \frac{\Gamma(\frac{n+m+1}{2})}{\Gamma(\frac{n+m}{2})} \sqrt{2 a}.$$

If $a \ne b$, I don't think there's such a neat answer. You can find or approximate the distribution of $a X + b Y$ in various ways, outlined in the answers to this question:

  • whuber's answer gives a way to get the exact distribution which you could numerically integrate to get the expected square root.
  • kjetil's answer gives code for a numerical approximation to the pdf, which again you could numerically integrate for the expected square root.
  • Paul's answer uses the Welch-Satterthwaite equation to approximate the sum as a gamma. Using that approximation, we get

    $$a X + b Y \stackrel{approx}{\sim} \Gamma\left( \frac{(m a + n b)^2}{2 (m a^2 + n b^2)}, 2 \frac{m a^2 + n b^2}{m a + n b} \right)$$

    which leads to

    $$ \mathbb E \sqrt{a X + b Y} \approx \frac{\Gamma\left( \frac{(m a + n b)^2}{2 (m a^2 + n b^2)} + \frac12 \right)}{\Gamma\left( \frac{(m a + n b)^2}{2 (m a^2 + n b^2)} \right)} \sqrt{ 2 \frac{m a^2 + n b^2}{m a + n b}} .$$

In any case, you should probably check your answer against a simple Monte Carlo simulation for the parameters you're interested in.

Danica
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  • Is there any analysis on the accuracy of Welch-Satterthwaite approximation? – user07001129 Apr 23 '17 at 00:05
  • @user07001129 I don't know of any offhand, and didn't find any in quick googling, though I didn't look very hard or read the original papers: https://dx.doi.org/10.2307%2F3002019 and https://dx.doi.org/10.2307%2F2332510, which may have some. I'd again recommend computing some Monte Carlo estimates for a few parameter values that you care about and seeing how they line up with the approximate answer. – Danica Apr 23 '17 at 18:34