Komlós–Major–Tusnády approximation
In probability theory, the Komlós–Major–Tusnády approximation (also known as the KMT approximation, the KMT embedding, or the Hungarian embedding) refers to one of the two strong embedding theorems: 1) approximation of random walk by a standard Brownian motion constructed on the same probability space, and 2) an approximation of the empirical process by a Brownian bridge constructed on the same probability space. It is named after Hungarian mathematicians János Komlós, Gábor Tusnády, and Péter Major, who proved it in 1975.
Theory
[edit]Let be independent uniform (0,1) random variables. Define a uniform empirical distribution function as
Define a uniform empirical process as
The Donsker theorem (1952) shows that converges in law to a Brownian bridge 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. the empirical process can be approximated by a sequence of Brownian bridges such that
- for all positive integers n and all , where a, b, and c are positive constants.
Corollary
[edit]A corollary of that theorem is that for any real iid r.v. with cdf it is possible to construct a probability space where independent[clarification needed] sequences of empirical processes and Gaussian processes exist such that
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References
[edit]- Komlos, J., Major, P. and Tusnady, G. (1975) An approximation of partial sums of independent rv’s and the sample df. I, Wahrsch verw Gebiete/Probability Theory and Related Fields, 32, 111–131. doi:10.1007/BF00533093
- Komlos, J., Major, P. and Tusnady, G. (1976) An approximation of partial sums of independent rv’s and the sample df. II, Wahrsch verw Gebiete/Probability Theory and Related Fields, 34, 33–58. doi:10.1007/BF00532688