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Komlós–Major–Tusnády approximation
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Komlós–Major–Tusnády approximation : ウィキペディア英語版
Komlós–Major–Tusnády approximation
In theory of probability, the Komlós–Major–Tusnády approximation (also known as the KMT approximation, the KMT embedding, or the Hungarian embedding) is an approximation of the empirical process by a Gaussian process constructed on the same probability space. It is named after Hungarian mathematicians János Komlós, Gábor Tusnády, and Péter Major.
==Theory==

Let U_1,U_2,\ldots be independent uniform (0,1) random variables. Define a uniform empirical distribution function as
:F_(t)=\frac\sum_^n \mathbf_,\quad t\in ().
Define a uniform empirical process as
:\alpha_(t)=\sqrt(F_(t)-t),\quad t\in ().
The Donsker theorem (1952) shows that \alpha_(t) converges in law to a Brownian bridge B(t). Komlós, Major and Tusnády established a sharp bound for the speed of this weak convergence.
:Theorem (KMT, 1975) On a suitable probability space for independent uniform (0,1) r.v. U_1,U_2\ldots the empirical process \ can be approximated by a sequence of Brownian bridges \ such that
::P\left\(t)-B_n(t)|>\frac\leq b e^
:for all positive integers ''n'' and all x>0, where ''a'', ''b'', and ''c'' are positive constants.

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