# Slutsky's theorem

{{ safesubst:#invoke:Unsubst||\$N=Refimprove |date=__DATE__ |\$B= {{#invoke:Message box|ambox}} }} In probability theory, Slutsky’s theorem[1] extends some properties of algebraic operations on convergent sequences of real numbers to sequences of random variables.

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

## Statement

Let {Xn}, {Yn} be sequences of scalar/vector/matrix random elements.

If Xn converges in distribution to a random element X;

and Yn converges in probability to a constant c, then

Notes:

1. In the statement of the theorem, the condition “Yn converges in probability to a constant c” may be replaced with “Yn converges in distribution to a constant c” — these two requirements are equivalent according to this property.
2. 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converges to a constant is important — if it were to converge to a non-degenerate random variable, the theorem would be no longer valid.
3. The theorem remains valid if we replace all convergences in distribution with convergences in probability (due to this property).

## 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−1y as continuous (for the last function to be continuous, x has to be invertible).

## References

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3. Slutsky's theorem is also called Cramér’s theorem according to Remark 11.1 (page 249) of Allan Gut. A Graduate Course in Probability. Springer Verlag. 2005.
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