# Mill's Inequality

## Background

One of the results in the paper I've been reading uses a tail bound on the max of Gaussian-distributed random variables that I was not too familiar with, so I thought I'd present and discuss it here to solidify my understanding of it.

## Statement

Suppose \(Z \sim \Nn(0, \sigma^2)\). Then we have the tail bound:

Intuitively, this kind of tail bound is useful because we can get exponentially-fast decay without calculating the distribution function directly.

## Proof

The broad strokes of the proof follow Aliyah Ahmed's response to a post on StackExchange. We begin by observing that density of \(Z\) is symmetric about the origin, therefore:

We then observe that by playing with distribution functions and expectations, we get the following upper bound:

in the process using sneaky way to introduce a quantity that has a nice, clean closed-form integral. Closer examination shows that this is in fact a tighter version of Markov's Inequality; rather than taking \(\EE X\), we take \(\EE [X \one\{X > t\}]\). This implies that:

## Extension to Sum of Random Variables

This result can be extended to the maximum of \(m\) Gaussian random variables by way of the union bound. Suppose \(\{Z_i\}_{i=1}^m \sim \Nn(0, \sigma^2)\). Then the union bound implies:

Suppose the variance of these random variables decreased with \(n\), i.e., \(\sigma^2 = \frac{1}{n}\). This could happen if our \(Z_i\) are estimators. Then we would have the bound:

Suppose we wanted to get a reasonably large probability on the right hand side so that our bound is useful. A trivial way to do this is to take \(t\) very small, but this upper bound would be meaningless.

What if we made \(t\) arbitrarily large? The implication in this case is not particularly useful either.

To balance between these interests, we can choose \(t\) such that:

giving us a bound that adapts to \(m\).