Previously, we discussed LLNs and tail bounds that apply to a collection of random variables taken together. In this blog, we mainly focus on uniform laws or uniform tail bounds.
1. Uniform convergence of the CDF
How can one estimate the CDF of an univariate random variable given a random sample?
Suppose we have $X_{1},…,X_{n}\sim F_{X}$, so a little bit of thought might suggest a natural estimator is the empirical CDF
, i.e.
where $\mathbb{I}$ is an indicator function. Unlike in a classical statistics problem we are not estimating a simple parameter, rather we are estimating an entire function.
Suppose we fixed a value $x$ and we decide to try to estimate $F_{X}(x)$. We could use the empirical CDF at $x$, but this time it is a rather simple prpbelm. Thus, we have:
The indicator are bounded random random variables so we could just use Hoffding’s bound to conclude that
This just show that for a single point $x$, we can use simple tail bounds to say that the empirical CDF is close to the true CDF. A more difficult question is to ask whether the empirical CDF and true CDF are close everywhere. In other words, we would like to know the behaviour of the worst case:
Reasoning about the $\bigtriangleup$ requires us to reason about the CDF everywhere, hence the name uniform bounds or uniform LLNs.
Notes:

The GlivenkoCantelli theorem is like a WLLN but it is a uniform WLLN that ensures essentially that the WLLN is true at every point $x\in \mathbb{R}$.

There is a corresponidng strong GC theorem that guarantee convergence almost surely.
Actually, we can estimate CDF of a random variable with no assumptions. This is constrast to estimating the density of a random variable which typically requires strong smoothness assumptions.
2. Equivalent forms, generalizations and empirical process theory
The empirical probability of a set $A$ is often denoted as:
The quantity $\bigtriangleup$ above can be equivalently written as,
where $\mathcal{A}$ is a collection of sets,
since in this case, $\mathbb{P}(A(x))=F_{X}(x)$.
To generalize the CDF question to more generally about other interesting collections of sets $\mathcal{A}$, i.e. we are interested in collections of sets $\mathcal{A}$, for which we have uniform convergence, i.e:
converges in probability to 0. This is well known as VapnikCervonenkis theory.
Even more generally, one can replace the indicators with general functions, i.e. let $\mathcal{F}$ be a class of integrable, realvalued functions, and suppose we have an i.i.d sample $X_{1},…,X_{n}\sim P$, then we could be intested in,
This quantity is known as an empirical process and empirical process theory is the area of statistics that asks questions about the convergence in probability, almost surely or in distribution for the quantity $\bigtriangleup(\mathcal{F})$ for interesting classes of functions of $\mathcal{F}$.
We refer to classes for which $\bigtriangleup(F)\overset{p}{\rightarrow}0,$ as GlivenkoCantelli classes. The class of functions:
which defines the uniform convergence of the CDF is an example of a GlivenkoCantelli class.
3. Failure of an uniform law
In generally, very complex classes of functions or sets will fail to be GlivenkoCantelli. Thus, we need some methods to measure the complexity of functions or sets. A failure case is shown here.
Suppose we draw $X_{1},…,X_{n}\sim P$ where $P$ is some continuous distribution over $[0,1]$. Suppose further that $\mathcal{A}$ is all subsets of $[0,1]$ with finitely many elements.
Then observe that since the distribution is continuous we have that, $\mathbb{P}(A)=0$ for each $A\in \mathcal{A}$, however for the finite set${X_{1},…,X_{n}}$ we have that $\mathbb{P}_{n}(A)=1$, i.e.
no matter what how large $n$ is. So the collection of sets $\mathcal{A}$ is not GlivenkoCantelli. Roughly, the collection of sets is “too large”.
4. Estimation of Statistical Functionals
Often we want to estimate some quantity which can be written as a simple functional of the CDF, and a natural estimate just replaces the true CDF with the empirical CDF(such estimators are known as plugin esimators). Functional is a function of a function. Here are some classical examples:
 Expectation Functionals: For a given function $g$, we can view the usual empirical estimator of its Expectation as a plugin estimate where we replace the population CDF by the empirical CDF,
 Quantile Functionals: For an $\alpha\in[0,1]$, the $\alphath$ quantile of a distribution is given as:
Taking $\alpha=0.5$ gives the median. A natural plugin estimator of $Q_{\alpha}(F)$ is to simply take $Q_{\alpha}(\widetilde{F}_{n})$.
 Goodnessoffit Functionals:
In data analysis, we want to test the hypothesis that we have are $i.i.d$ from some known distribution $F_{0}$. The rough idea is we form a statistic to test this hypothesis which(hopefully) takes large values when the distribution is not $F_{0}$ and takes small values otherwise. Typical tests of this form include the KolmogorovSmirnov test, where we compute the plugin quantity:
which is natural because if the true distribution is $F_{0}$ we know by the GlivenkoCantelli theorem that $T_{KS}$ is small. Similarly, one can use the Cramervon Mises test which uses the plugin statistic,
There are many other Statistical functionals for which the usual estimators can be thought of as plugin estimators. For example: variance, correlation, and higher moments can all be expressed in this function.
In each of the above cases we are interested in estimating some functional $\gamma(F)$ and we use the plugin estimator $\gamma(\widetilde{F}_{n})$. Analogous to the continuous mapping theorem, there is a GlivenkoCantelli theorem that provides a WLLN for these estimators. We need to first defines a notion of continuity. Suppose $\gamma$ satisfies the property that for every $\epsilon >0$, there is a $\delta >0$ such that if
then
for such functionals $\gamma$, it is a simple consequence of the GlivenkoCantelli theorem that $\gamma(\widetilde{F}_{n})$ converges in probability to $\gamma(F)$.