Higgs prime

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Uniform integrability is an important concept in real analysis, functional analysis and measure theory, and plays a vital role in the theory of martingales.

Formal definition

The following definition applies.[1]

  • An alternative definition involving two clauses may be presented as follows: A class 𝒞 of random variables is called uniformly integrable if:

Related corollaries

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  • Definition 1 could be rewritten by taking the limits as
limKsupX𝒞E(|X|I|X|K)=0.
  • A non-UI sequence. Let Ω=, and define
Xn(ω)={n,ω(0,1/n),0,otherwise.
Clearly XnL1, and indeed E(|Xn|)=1, for all n. However,
E(|Xn|,|Xn|K)=1 for all nK,
and comparing with definition 1, it is seen that the sequence is not uniformly integrable.
Non-UI sequence of RVs. The area under the strip is always equal to 1, but Xn0 pointwise.
  • By using Definition 2 in the above example, it can be seen that the first clause is not satisfied as the Xns are not bounded in L1. If X is a UI random variable, by splitting
E(|X|)=E(|X|,|X|>K)+E(|X|,|X|<K)
and bounding each of the two, it can be seen that a uniformly integrable random variable is always bounded in L1. It can also be shown that any L1 random variable will satisfy clause 2 in Definition 2.
  • If any sequence of random variables Xn is dominated by an integrable, non-negative Y: that is, for all ω and n,
|Xn(ω)||Y(ω)|,Y(ω)0,E(Y)<,
then the class 𝒞 of random variables {Xn} is uniformly integrable.
  • A class of random variables bounded in Lp (p>1) is uniformly integrable.

Relevant theorems

A class of random variables XnL1(μ) is uniformly integrable if and only if it is relatively compact for the weak topology σ(L1,L).
The family {Xα}αAL1(μ) is uniformly integrable if and only if there exists a non-negative increasing convex function G(t) such that
limtG(t)t= and supαE(G(|Xα|))<.

Relation to convergence of random variables

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  • A sequence {Xn} converges to X in the L1 norm if and only if it converges in measure to X and it is uniformly integrable. In probability terms, a sequence of random variables converging in probability also converge in the mean if and only if they are uniformly integrable.[4] This is a generalization of the dominated convergence theorem.

Citations

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References

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  • 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

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  • J. Diestel and J. Uhl (1977). Vector measures, Mathematical Surveys 15, American Mathematical Society, Providence, RI ISBN 978-0-8218-1515-1
  1. 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

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  2. Dellacherie, C. and Meyer, P.A. (1978). Probabilities and Potential, North-Holland Pub. Co, N. Y. (Chapter II, Theorem T25).
  3. Meyer, P.A. (1966). Probability and Potentials, Blaisdell Publishing Co, N. Y. (p.19, Theorem T22).
  4. 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

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