---
title: "Anderson-Darling k-Sample Test"
author: "Stefan Kloppenborg, Jeffrey Borlik"
date: "20-Jan-2019"
output: rmarkdown::html_vignette
bibliography: bibliography.json
csl: ieee.csl
vignette: >
  %\VignetteIndexEntry{Anderson-Darling k-Sample Test}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

This vignette explores the Anderson--Darling k-Sample test.
CMH-17-1G [@CMH-17-1G] provides a formulation for this test that appears different than the formulation given by Scholz and Stephens in their 1987 paper [@Stephens1987].

Both references use different nomenclature, which is summarized as follows:

Term                                               | CMH-17-1G             | Scholz and Stephens
---------------------------------------------------|-----------------------|---------------------
A sample                                           | $i$                   | $i$
The number of samples                              | $k$                   | $k$
An observation within a sample                     | $j$                   | $j$
The number of observations within the sample $i$   | $n_i$                 | $n_i$
The total number of observations within all samples| $n$                   | $N$
Distinct values in combined data, ordered          | $z_{(1)}$...$z_{(L)}$ | $Z_1^*$...$Z_L^*$
The number of distinct values in the combined data | $L$                   | $L$


Given the possibility of ties in the data, the discrete version of the test must be used
Scholz and Stephens (1987) give the test statistic as:

$$
A_{a k N}^2 = \frac{N - 1}{N}\sum_{i=1}^k \frac{1}{n_i}\sum_{j=1}^{L}\frac{l_j}{N}\frac{\left(N M_{a i j} - n_i B_{a j}\right)^2}{B_{a j}\left(N - B_{a j}\right) - N l_j / 4}
$$


CMH-17-1G gives the test statistic as:

$$
ADK = \frac{n - 1}{n^2\left(k - 1\right)}\sum_{i=1}^k\frac{1}{n_i}\sum_{j=1}^L h_j \frac{\left(n F_{i j} - n_i H_j\right)^2}{H_j \left(n - H_j\right) - n h_j / 4}
$$

By inspection, the CMH-17-1G version of this test statistic contains an extra factor of $\frac{1}{\left(k - 1\right)}$.

Scholz and Stephens indicate that one rejects $H_0$ at a significance level of $\alpha$ when:

$$
\frac{A_{a k N}^2 - \left(k - 1\right)}{\sigma_N} \ge t_{k - 1}\left(\alpha\right)
$$

This can be rearranged to give a critical value:

$$
A_{c r i t}^2 = \left(k - 1\right) + \sigma_N t_{k - 1}\left(\alpha\right)
$$

CHM-17-1G gives the critical value for $ADK$ for $\alpha=0.025$ as:

$$
ADC = 1 + \sigma_n \left(1.96 + \frac{1.149}{\sqrt{k - 1}} - \frac{0.391}{k - 1}\right)
$$

The definition of $\sigma_n$ from the two sources differs by a factor of $\left(k - 1\right)$.

The value in parentheses in the CMH-17-1G critical value corresponds to the interpolation formula for $t_m\left(\alpha\right)$ given in Scholz and Stephen's paper.
It should be noted that this is *not* the student's t-distribution, but rather a distribution referred to as the $T_m$ distribution.

The `cmstatr` package use the package `kSamples` to perform the k-sample Anderson--Darling tests.
This package uses the original formulation from Scholz and Stephens, so the test statistic will differ from that given software based on the CMH-17-1G formulation by a factor of $\left(k-1\right)$.

For comparison, [SciPy's implementation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.anderson_ksamp.html) also
uses the original Scholz and Stephens formulation.  The statistic that it returns, however, is the normalized statistic, 
$\left[A_{a k N}^2 - \left(k - 1\right)\right] / \sigma_N$, rather than `kSamples`'s $A_{a k N}^2$ value.  To be consistent, SciPy also returns the critical values $t_{k-1}(\alpha)$ directly.  (Currently, SciPy also floors/caps the returned p-value at 0.1% / 25%.)  The values of $k$ and $\sigma_N$ are available in `cmstatr`'s `ad_ksample` return value, if an exact comparison to Python SciPy is necessary.

The conclusions about the null hypothesis drawn, however, will be the same, whether R or CMH-17-1G or SciPy.

# References
