Assume $F_{n},F$ are distribution functions of r.v.$X_{n}$ and $X$, $F_{n}$ weakly converge to $F$. If $F$ is pointwise continuous in the interval $[a,b]\subset\mathbb{R}$, show that $$\sup_{x\in[a,b]}|F_{n}(x)-F(x)|\rightarrow 0,n\rightarrow \infty.$$Thanks.
1 Answers
The key is to prove that $F_n(x_n)\to F(x)$ for each sequence $(x_n,n\geqslant 1)$ converging to $x$. Indeed, once it is done, suppose that $F_n$ does not converge uniformly to $F$ on $[a,b]$. This means that for some positive $\delta$ and some sequence of integers $n_k\uparrow \infty$, $\sup_{x\in [a,b]}|F_{n_k}(x)-F(x)|>\delta$. Take $x_{n_k}$ such that $|F_{n_k}(x_{n_k})-F(x)|>\delta$. Extract a convergent subsequence of $(x_{n_k})$ to get a contradiction.
Passing to subsequences, one can deal with two cases:
- $x_n\uparrow x$;
- $x_n\downarrow x$.
We will thereat the first one. The idea is the following: write $$F_n(x_n)-F(x_n)=\mu(X_n\leqslant x)-\mu(x_n\lt X_n\leqslant x)-\mu(X\leqslant x)+\mu(x_n\lt X\leqslant x).$$ Notice that for each $n_0$ and $n\geqslant n_0$, $$\mu(x_n\lt X_n\leqslant x)\leqslant \mu(x_{n_0}\lt X_n\leqslant x),$$ hence $$\limsup_{n\to \infty}\mu(x_n\lt X_n\leqslant x)\leqslant\mu(x_{n_0}\lt X\leqslant x),$$ and since $F$ is continuous and $n_0$ arbitrary, we get $\lim_{n\to \infty}\mu(x_n\lt X_n\leqslant x)=0$.
Notice that we didn't use compactness: actually, we can prove that if $F$ is continuous on $\mathbb R$, there is uniform convergence of the sequence of cumulative distribution functions on the whole real line. In the argument, once we get the $\delta$, we take $R$ such that $\sup_n1-F_n(R)\lt\delta$ and $F(-R)\lt\delta$ in order to work with a compact interval.
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Yes,it can be generalize to on the whole real line. Thank you for your advice. I have figured out it. – Jacky Zhang Aug 17 '13 at 04:55
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You are welcome. – Davide Giraudo Aug 17 '13 at 09:00
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Hi,I understand the proof above, but I am missing the link through which this can be used to prove uniform convergence. – rajatsen91 Nov 05 '14 at 15:34
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@rajatsen91 I did not write a lot of details because the initial question didn't. I edited nevertheless. – Davide Giraudo Nov 06 '14 at 22:54
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Thanks a lot. I actually figured out the trick of bounding the tails of the cdf later. – rajatsen91 Nov 07 '14 at 02:10
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Could you explain more about the generalization onto the whole real line? That is the part that I am stuck at.. – Keith May 17 '15 at 09:17
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Can you elaborate on why it is sufficient to consider the cases $x_n\uparrow x$ and $x_n\downarrow x$? The "passing to subsequences" bit is unclear to me. Sorry to reply to such an early post. – Sri-Amirthan Theivendran Jan 23 '19 at 00:52
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@Sri-AmirthanTheivendran because cdf's are continous from the right. They inherit this from the continuity from above of probability measures. – xyz Sep 29 '22 at 14:57