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Slutsky's theorem

Theorem in probability theory From Wikipedia, the free encyclopedia

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In probability theory, Slutsky's theorem extends some properties of algebraic operations on convergent sequences of real numbers to sequences of random variables.[1]

The theorem was named after Eugen Slutsky.[2] Slutsky's theorem is also attributed to Harald Cramér.[3]

Statement

Summarize
Perspective

Let be sequences of scalar/vector/matrix random elements. If converges in distribution to a random element and converges in probability to a constant , then

  •   provided that c is invertible,

where denotes convergence in distribution.

Notes:

  1. The requirement that Yn converges to a constant is important — if it were to converge to a non-degenerate random variable, the theorem would be no longer valid. For example, let and . The sum for all values of n. Moreover, , but does not converge in distribution to , where , , and and are independent.[4]
  2. The theorem remains valid if we replace all convergences in distribution with convergences in probability.
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Proof

This theorem follows from the fact that if Xn converges in distribution to X and Yn converges in probability to a constant c, then the joint vector (Xn, Yn) converges in distribution to (X, c) (see here).

Next we apply the continuous mapping theorem, recognizing the functions g(x,y) = x + y, g(x,y) = xy, and g(x,y) = x y−1 are continuous (for the last function to be continuous, y has to be invertible).

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See also

References

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