V-statistic

1. The k-th central moment is the functional ${\displaystyle T(F)=\int (x-\mu )^{k}\,dF(x)}$, where ${\displaystyle \mu =E[X]}$ is the expected value of X. The associated statistical function is the sample k-th central moment,

   ${\displaystyle T_{n}=m_{k}=T(F_{n})={\frac {1}{n}}\sum _{i=1}^{n}(x_{i}-{\overline {x}})^{k}.}$

2. The chi-squared goodness-of-fit statistic is a statistical function T(F_n), corresponding to the statistical functional

   ${\displaystyle T(F)=\sum _{i=1}^{k}{\frac {(\int _{A_{i}}\,dF-p_{i})^{2}}{p_{i}}},}$

   where A_i are the k cells and p_i are the specified probabilities of the cells under the null hypothesis.

3. The Cramér–von-Mises and Anderson–Darling goodness-of-fit statistics are based on the functional

   ${\displaystyle T(F)=\int (F(x)-F_{0}(x))^{2}\,w(x;F_{0})\,dF_{0}(x),}$

   where w(x; F₀) is a specified weight function and F₀ is a specified null distribution. If w is the identity function then T(F_n) is the well known Cramér–von-Mises goodness-of-fit statistic; if ${\displaystyle w(x;F_{0})=[F_{0}(x)(1-F_{0}(x))]^{-1}}$ then T(F_n) is the Anderson–Darling statistic.

Suppose x₁, ..., x_n is a sample. In typical applications the statistical function has a representation as the V-statistic

${\displaystyle V_{mn}={\frac {1}{n^{m}}}\sum _{i_{1}=1}^{n}\cdots \sum _{i_{m}=1}^{n}h(x_{i_{1}},x_{i_{2}},\dots ,x_{i_{m}}),}$

where h is a symmetric kernel function. Serfling[6] discusses how to find the kernel in practice. V_mn is called a V-statistic of degree m.

A symmetric kernel of degree 2 is a function h(x, y), such that h(x, y) = h(y, x) for all x and y in the domain of h. For samples x₁, ..., x_n, the corresponding V-statistic is defined

${\displaystyle V_{2,n}={\frac {1}{n^{2}}}\sum _{i=1}^{n}\sum _{j=1}^{n}h(x_{i},x_{j}).}$

4. An example of a degree-2 V-statistic is the second central moment m₂. If h(x, y) = (x − y)²/2, the corresponding V-statistic is

   ${\displaystyle V_{2,n}={\frac {1}{n^{2}}}\sum _{i=1}^{n}\sum _{j=1}^{n}{\frac {1}{2}}(x_{i}-x_{j})^{2}={\frac {1}{n}}\sum _{i=1}^{n}(x_{i}-{\bar {x}})^{2},}$

   which is the maximum likelihood estimator of variance. With the same kernel, the corresponding U-statistic is the (unbiased) sample variance:

   ${\displaystyle s^{2}={n \choose 2}^{-1}\sum _{i<j}{\frac {1}{2}}(x_{i}-x_{j})^{2}={\frac {1}{n-1}}\sum _{i=1}^{n}(x_{i}-{\bar {x}})^{2}}$.