Robbins lemma

In statistics, the Robbins lemma, named after Herbert Robbins, states that if X is a random variable having a Poisson distribution with parameter λ, and f is any function for which the expected value E(f(X)) exists, then[1]

E ⁡ ( X f ( X − 1 ) ) = λ E ⁡ ( f ( X ) ) . {\displaystyle \operatorname {E} (Xf(X-1))=\lambda \operatorname {E} (f(X)).}

Robbins introduced this proposition while developing empirical Bayes methods.

References

  1. ^ Samaniego, Francisco J. (2015), Stochastic Modeling and Mathematical Statistics: A Text for Statisticians and Quantitative Scientists, CRC Press, p. 118, ISBN 9781466560475.