Empirical distribution process (${\alpha }_n(x)=\sqrt{n}(G_n(x)-G(x))$)

The empirical distribution process is the centred, scaled empirical distribution function of an independent sample with cumulative distribution function $G$. Its mean is zero and its covariance is $G(\min (x,y))-G(x)G(y)$. This follows by expanding the two indicator averages: different sample indices have zero covariance, while the same index contributes the covariance of the two nested events. The scaling by $\sqrt{n}$ keeps these covariances independent of $n$.

For an independent sample with cumulative distribution function $F$, the empirical distribution process converges in the supremum norm on ${\ell }^{\infty }(\mathbb{R})$ to the F-Brownian bridge. The convergence uses outer expectation and inner expectation when finite-sample Borel measurability fails. It is a full function-space theorem, stronger than the Finite-dimensional Brownian bridge limit of an empirical distribution process. No continuity of $F$ is required. The continuous mapping theorem gives convergence of the supremum statistic; for continuous $F$, its range contains $(0,1)$ and the bridge vanishes at zero and one, giving the Kolmogorov-Smirnov theorem. The function-space statement is stated here as a theorem; its tightness assertion is an additional ingredient beyond the finite-dimensional covariance calculation.

For fixed $x_1,\dots ,x_d$, the vector $({\alpha }_n(x_j){)}_{j=1}^d$ converges in distribution to $(B(G(x_j)){)}_{j=1}^d$, where $B$ is a standard Brownian bridge. To prove it, write the vector as $n^{-1/2}$ times the sum of independent centred bounded indicator vectors. Their covariance matrix is $G(\min (x_i,x_j))-G(x_i)G(x_j)$. The multivariate central limit theorem gives a centred multivariate normal distribution with this covariance, exactly the bridge vector. Singular covariance matrices, including endpoint times or repeated values of $G$, cause no problem. This is a finite-dimensional assertion; a full function-space limit additionally requires tightness.