k-sample test

Consider \(k\) random samples of i.i.d. observations \(\mathbf{x}^{(i)}_1, \mathbf{x}^{(i)}_{2}, \ldots, \mathbf{x}^{(i)}_{n_i} \sim F_i\), for \(i = 1, \ldots, k\).

We test if the samples are generated from the same unknown distribution \(\bar{F}\), that is: \[ H_0: F_1 = F_2 = \ldots = F_k = \bar{F} \] versus the alternative where two of the \(k\) distributions differ, that is \[ H_1: F_i \not = F_j, \] for some \(1 \le i \not = j \le k\).

Upon the construction of a matrix distance \(\hat{\mathbf{D}}\), with off-diagonal elements \[ \hat{D}_{ij} = \frac{1}{n_i n_j} \sum_{\ell=1}^{n_i}\sum_{r=1}^{n_j}K_{\bar{F}}(\mathbf{x}^{(i)}_\ell,\mathbf{x}^{(j)}_r), \qquad \mbox{ for }i \not= j \] and in the diagonal \[ \hat{D}_{ii} = \frac{1}{n_i (n_i -1)} \sum_{\ell=1}^{n_i}\sum_{r\not= \ell}^{n_i}K_{\bar{F}}(\mathbf{x}^{(i)}_\ell,\mathbf{x}^{(i)}_r), \qquad \mbox{ for }i = j, \] where \(K_{\bar{F}}\) denotes the Normal kernel \(K\), defined as

\[ K(\mathbf{s}, \mathbf{t}) = (2 \pi)^{-d/2} \left(\det{\mathbf{\Sigma}_h}\right)^{-\frac{1}{2}} \exp\left\{-\frac{1}{2}(\mathbf{s} - \mathbf{t})^\top \mathbf{\Sigma}_h^{-1}(\mathbf{s} - \mathbf{t})\right\}, \]

for every \(\mathbf{s}, \mathbf{t} \in \mathbb{R}^d \times \mathbb{R}^d\), with covariance matrix \(\mathbf{\Sigma}_h = h^2 I\) and tuning parameter \(h\), centered with respect to \[ \bar{F} = \frac{n_1 \hat{F}_1 + \ldots + n_k \hat{F}_k}{n}, \quad \mbox{ with } n=\sum_{i=1}^k n_i. \] For more information about the centering of the kernel, see the documentation of the kb.test() function.

help(kb.test) 

We compute the trace statistic \[ \mathrm{trace}(\hat{\mathbf{D}}_n) = \sum_{i=1}^{k}\hat{D}_{ii}. \] and \(D_n\), derived considering all the possible pairwise comparisons in the \(k\)-sample null hypothesis, given as \[ D_{n} = (k-1) \mathrm{trace}(\hat{\mathbf{D}}_n) - 2 \sum_{i=1}^{k}\sum_{j> i}^{k}\hat{D}_{ij}. \]

We show the usage of the kb.test() function with the following example of \(k=3\) samples of bivariate observations following normal distributions with different mean vectors.

We generate three samples, with \(n=50\) observations each, from a 2-dimensional Gaussian distributions with mean vectors \(\mu_1 = (0, \frac{\sqrt{3}}{3})\), \({\mu}_2 = (-\frac{1}{2}, -\frac{\sqrt{3}}{6})\) and \(\mu_3 = (\frac{1}{2}, -\frac{\sqrt{3}}{6})\), and the Identity matrix as covariance matrix. In this situation, the generated samples are well separated, following different Gaussian distributions, i.e. \(X_1 \sim N_2(\mu_1, I)\), \(X_2 \sim N_2(\mu_2, I)\) and \(X_3 \sim N_2(\mu_3, I)\)}. In order to perform the \(k\)-sample tests, we need to define the vector y which indicates the membership to groups.

library(mvtnorm)
library(QuadratiK)
library(ggplot2)
sizes <- rep(50,3)
eps <- 1
set.seed(2468)
x1 <- rmvnorm(sizes[1], mean = c(0,sqrt(3)*eps/3))
x2 <- rmvnorm(sizes[2], mean = c(-eps/2,-sqrt(3)*eps/6))
x3 <- rmvnorm(sizes[3], mean = c(eps/2,-sqrt(3)*eps/6))
x <- rbind(x1, x2, x3)
y <- as.factor(rep(c(1,2,3), times=sizes))

ggplot(data.frame(x=x, y=y), aes(x = x[,1], y = x[,2], color = y)) +
  geom_point(size = 2) +
  labs(title = "Generated Points", x = "X1", y = "X2") +
  theme_minimal()

To use the kb.test() function, we need to provide the value for the tuning parameter \(h\). The function select_h can be used for identifying on optimal value of \(h\). This function needs the input x and y as the function kb.test, and the selection of the family of alternatives. Here we consider the location alternatives.

set.seed(2468)
h_k <- select_h(x=x, y=y, alternative="location")

h_k$h_sel

## [1] 1.2

The select_h function has also generated a figure displaying the obtained power versus the considered \(h\), for each value of alternative \(\delta\) considered.

We can now perform the \(k\)-sample tests with the optimal value of \(h\).

set.seed(2468)
k_test <- kb.test(x=x, y=y, h=h_k$h_sel)
show(k_test)

## 
##  Kernel-based quadratic distance k-sample test 
## U-statistic   Dn          Trace 
## ------------------------------------------------
## Test Statistic:   3.76127     5.552056 
## Critical Value:   1.270525    1.876836 
## H0 is rejected:   TRUE        TRUE 
## CV method:  subsampling 
## Selected tuning parameter h:  1.2

The function kb.test() returns an object of class kb.test. The show method for the kb.test object shows the computed statistics with corresponding critical values, and the logical indicating if the null hypothesis is rejected. The test correctly rejects the null hypothesis, in fact the values of the statistics are greater than the computed critical values. The package provides also the summary function which returns the results of the tests together with the standard descriptive statistics for each variable computed, overall, and with respect to the provided groups.

summary_ktest <- summary(k_test)

## 
##  Kernel-based quadratic distance k-sample test 
##   Statistic    Value Critical_Value Reject_H0
## 1        Dn 3.761270       1.270525      TRUE
## 2     Trace 5.552056       1.876836      TRUE

summary_ktest$summary_tables

## [[1]]
##            Group 1    Group 2    Group 3      Overall
## mean   -0.05208816 -0.3961768  0.5318161  0.027850399
## sd      0.96223294  0.8169982  1.1147943  1.039422979
## median -0.07433374 -0.4171737  0.4466713  0.003313025
## IQR     1.34379740  1.1499518  1.4976634  1.507024820
## min    -2.86000669 -2.1929616 -2.1754778 -2.860006689
## max     1.88750642  1.0851059  2.6517848  2.651784802
## 
## [[2]]
##           Group 1    Group 2    Group 3     Overall
## mean    0.3928294 -0.2851004 -0.4028292 -0.09836674
## sd      0.9612003  1.1243216  0.9603282  1.07079458
## median  0.2303015 -0.1667130 -0.3676814 -0.14246592
## IQR     1.1269249  1.2443774  1.3256384  1.24637078
## min    -1.1662595 -3.5108957 -2.6488286 -3.51089574
## max     3.0792766  2.1192756  1.5225887  3.07927659

k_test_h <- kb.test(x=x, y=y)

help(select_h)