# How often two iid variables are close?

Is there a constant $$c>0$$ such that for $$X,Y$$ two iid variables supported by $$[0,1]$$, $$\liminf_\epsilon \epsilon^{-1}P(|X-Y|<\epsilon)\geqslant c$$ I can prove the result if they have a density, of if they have atoms, but not in the general case.

If $$\epsilon \geqslant \tfrac{1}{n}$$, then $$\mathbb{P}(|X-Y|<\epsilon) \geqslant \sum_{i=1}^n \mathbb{P}(X, Y \in [\tfrac{i-1}{n}, \tfrac{i}{n}]) = \sum_{i=1}^n (\mathbb{P}(X \in [\tfrac{i-1}{n}, \tfrac{i}{n}]))^2 .$$ It follows that $$\mathbb{P}(|X-Y|<\epsilon) \geqslant \frac{1}{n} \biggl(\sum_{i=1}^n \mathbb{P}(X \in [\tfrac{i-1}{n}, \tfrac{i}{n}])\biggr)^2 = \frac{1}{n} \, .$$ If we choose $$n$$ so that additionally $$\epsilon < \frac{1}{n-1}$$, then we obtain $$\mathbb{P}(|X-Y|<\epsilon) > \frac{\epsilon}{\epsilon + 1} \, .$$ This leads to the desired result with $$c = 1$$.

• Interestingly, while the estimate given above is sharp at least when $\epsilon = \tfrac{1}{n}$ (consider a uniform distribution over $\frac{i}{n - 1}$, $i = 0, 1, \ldots, n - 1$), my guess is that the optimal value of $c$ is in fact $2$. However, I fail to see a proof. – Mateusz Kwaśnicki Mar 10 at 21:44

Mateusz Kwaśnicki's guess, that the optimal value of $$c$$ is 2, is correct! In fact:

Theorem: Suppose $$X,Y$$ are i.i.d. random variables on $$\mathbb{R}$$. Then the limit $$\lim_{\varepsilon \rightarrow 0} \varepsilon^{-1} \mathbb{P}[|X - Y| < \varepsilon]$$ exists. It is $$+\infty$$ unless $$X$$ has a density function $$f$$ satisfying $$||f||_2^2 = \int f^2\ \text{d}x < \infty$$, in which case the limit is equal to $$2 ||f||_2^2$$.

Restricting to distributions on $$[0,1]$$, Cauchy-Schwarz says $$1 = \int_0^1 f\ \text{d}x \le \left ( \int_0^1 f^2\ \text{d}x \right )^{1/2} \left ( \int_0^1 1\ \text{d}x \right )^{1/2} = ||f||_2,$$ with equality iff $$f = 1$$. That is, the constant $$c = 2$$ is valid for every distribution, and it is sharp only for the uniform distribution.

Here are a few examples where the limit is infinite:

Example 1: If $$X$$ has an atom then $$\mathbb{P}[|X-Y| < \varepsilon]$$ is bounded away from 0. So the limit in question diverges like $$\varepsilon^{-1}$$.

Example 2: The Cantor ternary function is the CDF of a probability distribution on $$[0,1]$$ which is nonatomic but not absolutely continuous. A random sample from this distribution is given by $$\sum_{k \ge 1} c_k \tfrac{2}{3^k}$$ where $$\{c_k\}$$ are i.i.d. Bernoulli random variables. (Thought of in terms of the "repeatedly take out the middle third" construction of the Cantor set, the $$c_k$$'s are the choices of left versus right third.) If $$\varepsilon = 3^{-n}$$, then $$|X-Y| < \varepsilon$$ iff the first $$n$$ $$c_k$$'s agree. Thus $$\mathbb{P}[|X-Y| < \varepsilon] = 2^{-n} = \varepsilon^{\log_3 2}.$$ So the limit in question diverges like $$\varepsilon^{-1+\log_3 2} = \varepsilon^{-0.37}$$.

Example 3: The power law $$f(x) = \tfrac{1}{2\sqrt{x}}$$ is an example of a density function which is not in $$L^2$$. In this case we can evaluate $$\mathbb{P}[|X-Y| < \varepsilon]$$ exactly (well, if you believe in inverse hyperbolic trig functions). The asymptotic behavior is $$\mathbb{P}[|X-Y| < \varepsilon] = \tfrac{1}{2} (-\log \varepsilon) \varepsilon + (\tfrac{1}{2} + \log 2) \varepsilon + O(\varepsilon^2).$$ So the limit in question diverges logarithmically.

Mateusz's slick argument worked on the block diagonal, a sum of $$n$$ squares of width $$1/n$$. This covers about half of the area of the strip $$|X-Y| < \tfrac{1}{n}$$, which is why the resulting constant is half of optimal. There's probably a hands-on way to extend it, but you start using words like "convolution" and I found it easier to reason in Fourier space. This question is asking for a bound on the density of $$X-Y$$ at 0. This random variable has nonnegative Fourier transform (characteristic function, if you prefer), and that condition alone is enough to guarantee positive density (shameless self-citation: Lemma 3.1 in this paper). In general, when it makes sense, the density at 0 of a random variable is equal to the integral of its Fourier transform. So our job is to make that concrete and translate it back to a statement about PDFs.

Convention: The Fourier transform (characteristic function) of a random variable $$X$$ is the function $$t \mapsto \mathbb{E}[e^{2 \pi \mathrm{i} X t}]$$. The Fourier transform of a function $$f$$ is $$t \mapsto \hat f(t) = \int e^{2 \pi \mathrm{i} x t} f(x)\ \text{d}x$$. If $$X$$ is absolutely continuous (has a density function), then its Fourier transform is equal to the Fourier transform of its density function. With this convention the Plancherel identity reads $$\int f \bar g\ \text{d}x = \int \hat f \overline{\hat g}\ \text{d}t$$ (no normalization constant).

Lemma: Let $$\psi$$ denote the Fourier transform of $$X$$. Then $$\psi \in L^2$$ iff $$X$$ is absolutely continuous and its density function $$f$$ is in $$L^2$$. Moreover, if this holds, then $$||\psi||_2 = ||f||_2$$.

Proof of Lemma: The Fourier transform is an isometry of the space $$L^2$$. The ($$\Leftarrow$$) direction is immediate: if $$X$$ has a density function in $$L^2$$, then certainly its Fourier transform is in $$L^2$$. For ($$\Rightarrow$$), if $$\psi$$ is in $$L^2$$ then it is the Fourier transform of some function $$f \in L^2$$. But then $$f$$ defines the same distribution as $$X$$, so it is indeed the density function. $$\square$$

(This is probably a basic result in some probability textbook?)

Proof of Theorem: Let $$\psi$$ denote the Fourier transform of $$X$$. Then the Fourier transform of $$X-Y$$ is $$t \mapsto \psi(t) \psi(-t) = \psi(t) \overline{\psi(t)} = |\psi(t)|^2$$. Let $$T_\varepsilon$$ denote the "triangle filter" $$T_\varepsilon(x) = \varepsilon^{-1} (1 - |x|/\varepsilon)$$ for $$|x| \le \varepsilon$$ and $$T_\varepsilon(x) = 0$$ otherwise. It is a standard calculation that $$\hat T_\varepsilon(t) = \operatorname{sinc}^2(\pi t \varepsilon)$$. (Here $$\operatorname{sinc} x = \tfrac{\sin x}{x}$$.) This is integrable, so we can compute the expected value of $$T_\varepsilon$$ using the Fourier transform. We find $$\varepsilon^{-1} \mathbb{P}[|X-Y| < \varepsilon] \ge \mathbb{E}[T_\varepsilon(X-Y)] = \int |\psi(t)|^2 \operatorname{sinc}^2(\pi t \varepsilon)\ \text{d}t.$$ The integrand is nonnegative and, as $$\varepsilon \to 0$$, it converges (pointwise) to its (pointwise) supremum $$|\psi(t)|^2$$. So, by an appropriate incantation of the monotone convergence theorem (the one in this comment applies exactly), the integral converges to $$\int |\psi(t)|^2\ \text{d}t = ||\psi||_2^2$$. If this is infinite, then also $$\lim_{\varepsilon \to 0} \varepsilon^{-1} \mathbb{P}[|X-Y| < \varepsilon] = +\infty$$.

Suppose now that $$||\psi||_2^2 < \infty$$, i.e., $$\psi \in L^2$$. Consider the "box filter" $$B_\varepsilon$$ defined by $$B_\varepsilon(x) = (2 \varepsilon)^{-1}$$ for $$|x| < \varepsilon$$ and $$B_\varepsilon(x) = 0$$ otherwise. This has $$\hat B_\varepsilon(t) = \operatorname{sinc}(2 \pi t \varepsilon)$$. This is bounded and $$|\psi|^2$$ is integrable. So we can again compute expected values in Fourier space: $$(2\varepsilon)^{-1} \mathbb{P}[|X-Y| < \varepsilon] = \mathbb{E}[B_\varepsilon(X-Y)] = \int |\psi(t)|^2 \operatorname{sinc}(2 \pi t \varepsilon)\ \text{d}t.$$ The integrand converges pointwise to $$|\psi(t)|^2$$. We don't have nonnegativity this time, but now we know integrability of the bounding function $$|\psi|^2$$. So we can apply the dominated convergence theorem, concluding that $$\lim_{\varepsilon \to 0} (2 \varepsilon)^{-1} \mathbb{P}[|X-Y| < \varepsilon] = \int |\psi(t)|^2\ \text{d}t = ||\psi||_2^2.$$ The lemma completes the proof. $$\square$$