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Interval-dividing processes
Authors:Sam Gutmann
Institution:(1) Dept. of Mathematics, Northeastern University, 360 Huntington Avenue, 02115 Boston, MA, USA
Abstract:Summary If 
$$\forall n\sum\limits_\pi  {P(X_{\pi _1 }  < ... < } X_{\pi _n } ) = 1$$
and 
$$\forall \pi ,n,{\text{ }}P(X_{\pi _1 }  < ... < X_{\pi _n } ) = P(Y_{\pi _1 }  < ... < Y_{\pi _n } )$$
then P(n –1·delta(Y 1)+ctdot+delta(Y n )] converges to cnts. law on R 1) = P(n –1·delta(Y 1)+ctdot+delta(Y n )] converges to a cnts. law on R 1). Thus if 
$$P(X_{\pi _1 }  < ... < X_{\pi _n } ) = (n!)^{ - 1} \forall \pi ,n$$
,n then n –1delta(X 1)+...+delta(X n )] converges a.s. The main result here generalizes this: Let X (1) n , X (2) n ,..., X (n) n be the order statistics associated with X 1, X 2,ctdot,X n. Define random variables Z 1,Z 2,ctdot by {Z n =i}={X n =X (i) n }. Then if Z 1,Z 2,Z 3, ctdot are independent and P(ZnlEi)lEi/n, and {X i} is bounded, n –1·delta(X 1)+ctdot+delta(X n)] converges a.s.
Keywords:
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