This is an interesting problem from ISI MSTAT PSB 2011 Problem 4 that tests the student’s knowledge of how he visualizes the normal distribution in higher dimensions.

## The Problem: ISI MSTAT PSB 2011 Problem 4

Suppose that \( X_1,X_2,… \) are independent and identically distributed \(d\) dimensional normal random vectors. Consider a fixed \( x_0 \in \mathbb{R}^d \) and for \(i=1,2,…,\) define \(D_i = \| X_i – x_0 \| \), the Euclidean distance between \( X_i \) and \(x_0\). Show that for every \( \epsilon > 0 \), \(P[\min_{1 \le i \le n} D_i > \epsilon] \rightarrow 0 \) as \( n \rightarrow \infty \)

## Prerequisites:

- Finding the distribution of the minimum order statistic
- Multivariate Gaussian properties

## Solution:

First of all, see that \( P(\min_{1 \le i \le n} D_i > \epsilon)=P(D_i > \epsilon)^n \) (Verify yourself!)

But, apparently we are more interested in the event \( \{D_i < \epsilon \} \).

Let me elaborate why this makes sense!

Let \( \phi \) denote the \( d \) dimensional Gaussian density, and let \( B(x_0, \epsilon) \) be the Euclidean ball around \( x_0 \) of radius \( \epsilon \) . Note that \( \{D_i < \epsilon\} \) is the event that the gaussian \( X_i \) will land in this Euclidean ball.

So, if we can show that this event has positive probability for any given $x_0, \epsilon$ pair, we will be done, since then in the limit, we will be exponentiating a number strictly less than 1 by a quantity that is growing larger and larger.

In particular, we have that : \( P(D_i < \epsilon)= \int_{B(x_0, \epsilon)} \phi(x) dx \geq |B(x_0, \epsilon)| \inf_{x \in B(x_0, \epsilon)} \phi(x) \) , and we know that by rotational symmetry and as Gaussians decay as we move away from the centre, this infimum exists and is given by \( \phi(x_0 + \epsilon \frac{x_0}{||x_0||}) \) . (To see that this is indeed a lower bound, note that \( B(x_0, \epsilon) \subset B(0, \epsilon + ||x_0||) \).

So, basically what we have shown here is that exists a \( \delta > 0 \) such that \( P(D_i < \epsilon )>\delta \).

As, \( \delta \) is a lower bound of a probability , hence it a fraction strictly below 1.

Thus, we have \( \lim_{n \rightarrow \infty} P(D_i > \epsilon)^n \leq \lim_{n \rightarrow \infty} (1-\delta)^n = 0 \).

Hence we are done.

## Food for thought:

There is a fantastic amount of statistical literature on the equi-density contours of a multivariate Gaussian distribution .

Try to visualize them for non singular and a singular Gaussian distribution separately. They are covered extensively in the books of Kotz and Anderson. Do give it a read!

Anderson has several books and two of them are multivariate and elliptical countours. Would you like to say which one do you mention?

I mean the book An Introduction To Multivariate Statistics