# Quantile convergence

Let $X^1,\dots,X^n$ be a sample of (not necessarily iid) random variables and denote $$F^n(x)=\frac{1}{n}\sum_{i=1}^n \mathbf 1_{X^i\leq x}$$ the empirical distribution function. Suppose that we know that $F^n(x)\to F(x)$ in probability for all continuity points $x$ of $F$, where $F$ is a given cdf. Denote $$Q^n(p)=\inf\{x\in\mathbb R: F^n(x)\geq p\}, \quad Q(p)=\inf\{x\in\mathbb R: F(x)\geq p\}.$$ Can we say that $Q^n(p)\to Q(p)$ in probability for all continuity points $p$ of $Q$?

• Is the convergence in probability important, as compared to in distribution? If the latter suffices then I am quite sure that the treatment in van der Vaart & Wellner will give you what you need. They prove Hadamard differentiability of the generalized inverse functional and then the functional delta method provides the rest. However, if it is crucial that the convergence be in probability I would have to take a closer look at the arguments (hence, for now a comment rather than an answer). Jun 16 '14 at 20:58