Edgeworth Expansion for the Mann-Whitney U Test, Part 2: Increased Accuracy

In the previous post, we showed how the Edgeworth expansion can improve the accuracy of obtained p-values in the Mann-Whitney U test. However, we considered only the Edgeworth expansion to terms of order $1/m$. In this post, we explore how to improve the accuracyk of this approach using the Edgeworth expansion to terms of order $1/m^2$.

Extended Edgeworth approximation

In this post, we follow the approach from [Fix1955] to get various Edgeworth expansions for the Mann-Whitney U distribution. We denote the previously considered expansion by $p_{E3}$, and the extended one by $p_{E7}$. These expansions are defined as follows:

$$ p_{E3}(z) = \Phi(z) + e^{(3)} \varphi^{(3)}(z), $$ $$ p_{E7}(z) = \Phi(z) + e^{(3)} \varphi^{(3)}(z) + e^{(5)} \varphi^{(5)}(z) + e^{(7)} \varphi^{(7)}(z). $$

The Edgeworth coefficients $e^{(3)}$, $e^{(5)}$, $e^{(7)}$ are given by

$$ e^{(3)} = \frac{1}{4!}\left( \frac{\mu_4}{\mu_2^2} - 3 \right),\quad e^{(5)} = \frac{1}{6!}\left( \frac{\mu_6}{\mu_2^3} - 15\frac{\mu_4}{\mu_2^2} + 30 \right),\quad e^{(7)} = \frac{35}{8!}\left( \frac{\mu_4}{\mu_2^2} - 3 \right)^2, $$

where $\mu_k$ is the $k^\textrm{th}$ central moment of the Mann-Whitney U distribution:

$$ \mu_2 = \frac{nm(n+m+1)}{12}, $$ $$ \mu_4 = \frac{mn(m+n+1)}{240} \bigl( 5(m^2 n + m n^2) - 2(m^2 + n^2) + 3mn - (2m + n) \bigr), $$ $$ \begin{split} \mu_6 = \frac{mn(m+n+1)}{4032} \bigl( 35m^2 n^2 (m^2 + n^2) + 70 m^3 n^3 - 42 mn (m^3 + n^3) - 14 m^2 n^2 (m + n) +\\ + 16 (m^4 + n^4) - 52 mn (m^2 + n^2) - 43 m^2 n^2 + 32 (m^3 + n^3) +\\ + 14 mn (m + n) + 8 (m^2 + n^2) + 16 mn - 8 (m + n) \bigr). \end{split} $$

The terms $\varphi^{(3)}$, $\varphi^{(5)}$, $\varphi^{(7)}$ are the derivatives of the standard normal distribution density function $\varphi$. They can be expressed using the Hermite polynomials:

$$ \varphi^{(k)}(z) = -\varphi(z) H_k(z), $$ $$ H_3(z) = z^3 - 3z, $$ $$ H_5(z) = z^5 - 10z^3 + 15z, $$ $$ H_7(z) = z^7 - 21z^5 + 105z^3 - 105z. $$

Numerical simulations

Now let’s explore the accuracy of $p_{E3}$ and $p_{E7}$ against the normal approximation:

As we can see, $p_{E7}$ looks much better than $p_{E3}$ (especially in the middle part).

Now let’s look at the original p-values in some additional cases (logarithmic scale is used):

As we can see, $p_{E7}$ has a broader range of values, for which it produces more accurate results. However, it can behave worse than the normal distribution at the distribution tails.


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