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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 [[Martingale (probability theory)|martingales]]. | |||
==Formal definition== | |||
The following definition applies.<ref>{{cite book|last=Williams|first=David|title=Probability with Martingales|year=1997|publisher=Cambridge Univ. Press.|location=Cambridge|isbn=978-0-521-40605-5|pages=126–132|url=http://www.amazon.com/Probability-Martingales-Cambridge-Mathematical-Textbooks/dp/0521406056|edition=Repr.}}</ref> | |||
* A class <math>\mathcal{C}</math> of [[random variable]]s is called '''uniformly integrable''' (UI) if given <math>\epsilon>0</math>, there exists <math>K\in[0,\infty)</math> such that <math>E(|X|I_{|X|\geq K})\le\epsilon\ \text{ for all X} \in \mathcal{C}</math>, where <math> I_{|X|\geq K} </math> is the [[indicator function]] <math> I_{|X|\geq K} = \begin{cases} 1 &\text{if } |X|\geq K, \\ 0 &\text{if } |X| < K. \end{cases}</math>. | |||
* An alternative definition involving two clauses may be presented as follows: A class <math>\mathcal{C}</math> of random variables is called '''uniformly integrable''' if: | |||
** There exists a finite <math>K</math> such that, for every <math>X</math> in <math>\mathcal{C}</math>, <math>\mathrm E(|X|)\leqslant K</math>. | |||
** For every <math>\epsilon > 0</math> there exists <math>\delta > 0</math> such that, for every measurable <math>A</math> such that <math>\mathrm P(A)\leqslant \delta</math> and every <math>X</math> in <math>\mathcal{C}</math>, <math>\mathrm E(|X|:A)\leqslant\epsilon</math>. | |||
==Related corollaries== | |||
The following results apply.{{Citation needed|date=April 2012}} | |||
* Definition 1 could be rewritten by taking the limits as | |||
:: <math>\lim_{K \to \infty} \sup_{X \in \mathcal{C}} E(|X|I_{|X|\geq K})=0.</math> | |||
* A non-UI sequence. Let <math>\Omega = \mathbb{R}</math>, and define | |||
::<math>X_n(\omega) = \begin{cases} | |||
n, & \omega\in (0,1/n), \\ | |||
0 , & \text{otherwise.} \end{cases}</math> | |||
:Clearly <math>X_n\in L^1</math>, and indeed <math>E(|X_n|)=1\ ,</math> for all ''n''. However, | |||
::<math>E(|X_n|,|X_n|\ge K)= 1\ \text{ for all } n\ge K,</math> | |||
:and comparing with definition 1, it is seen that the sequence is not uniformly integrable. | |||
[[File:Uniform integrability.png|thumb|Non-UI sequence of RVs. The area under the strip is always equal to 1, but <math>X_n \to 0</math> pointwise.]] | |||
* By using Definition 2 in the above example, it can be seen that the first clause is not satisfied as the <math>X_n</math>s are not bounded in <math>L^1</math>. If <math>X</math> is a '''UI''' random variable, by splitting | |||
::<math>E(|X|)=E(|X|,|X|>K)+E(|X|,|X|<K)</math> | |||
:and bounding each of the two, it can be seen that a uniformly integrable random variable is always bounded in <math>L^1</math>. It can also be shown that any <math>L^1</math> random variable will satisfy clause 2 in Definition 2. | |||
* If any sequence of random variables <math>X_n</math> is dominated by an integrable, non-negative <math>Y</math>: that is, for all ω and ''n'', | |||
::<math>\ |X_n(\omega)| \le |Y(\omega)|,\ Y(\omega)\ge 0,\ E(Y)< \infty,</math> | |||
:then the class <math>\mathcal{C}</math> of random variables <math>\{X_n\}</math> is uniformly integrable. | |||
* A class of random variables bounded in <math>L^p</math> (<math>p>1</math>) is uniformly integrable. | |||
==Relevant theorems== | |||
* '''[[Nelson Dunford|Dunford]]–[[Billy James Pettis|Pettis]] theorem'''<ref>[[Claude Dellacherie and Paul-André Meyer|Dellacherie, C. and Meyer, P.A.]] (1978). ''Probabilities and Potential'', North-Holland Pub. Co, N. Y. (Chapter II, Theorem T25).</ref> | |||
:A class of random variables <math>X_n \subset L^1(\mu)</math> is uniformly integrable if and only if it is [[relatively compact]] for the [[weak topology]] <math>\sigma(L^1,L^\infty)</math>. | |||
*'''[[Charles Jean de la Vallée-Poussin|de la Vallée-Poussin]] theorem'''<ref>[[Paul-André Meyer|Meyer, P.A.]] (1966). ''Probability and Potentials'', Blaisdell Publishing Co, N. Y. (p.19, Theorem T22).</ref> | |||
:The family <math>\{X_{\alpha}\}_{\alpha\in\Alpha} \subset L^1(\mu)</math> is uniformly integrable if and only if there exists a non-negative increasing convex function <math>G(t)</math> such that | |||
::<math>\lim_{t \to \infty} \frac{G(t)}{t} = \infty</math> and <math>\sup_{\alpha} E(G(|X_{\alpha}|)) < \infty.</math> | |||
==Relation to convergence of random variables== | |||
{{main|convergence of random variables}} | |||
* A sequence <math>\{X_n\}</math> converges to <math>X</math> in the <math>L_1</math> norm if and only if it [[convergence in measure|converges in measure]] to <math>X</math> 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.<ref>{{cite book|last=Bogachev|first=Vladimir I.|title=Measure Theory Volume I|year=2007|publisher=Springer-Verlag|location=Berlin Heidelberg|isbn=3-540-34513-2|pages=268|url=http://www.amazon.com/Measure-Theory-2-Volume-Set/dp/3540345132|doi=10.1007/978-3-540-34514-5_4}}</ref> This is a generalization of the [[dominated convergence theorem]]. | |||
==Citations== | |||
{{Reflist}} | |||
==References== | |||
* {{cite book|authorlink=Albert Nikolayevich Shiryaev|author=A.N. Shiryaev|year=1995|title=Probability|edition=2|publisher=Springer-Verlag|location=New York|pages=187–188|isbn=978-0-387-94549-1}} | |||
* {{cite book|author=Walter Rudin|authorlink=Walter Rudin|year=1987|title=Real and Complex Analysis|edition=3|publisher=McGraw–Hill Book Co.|location=Singapore|page=133|isbn=0-07-054234-1}} | |||
* J. Diestel and J. Uhl (1977). ''Vector measures'', Mathematical Surveys 15, American Mathematical Society, Providence, RI ISBN 978-0-8218-1515-1 | |||
[[Category:Probability theory]] |
Latest revision as of 18:52, 16 March 2013
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]
- A class of random variables is called uniformly integrable (UI) if given , there exists such that , where is the indicator function .
- An alternative definition involving two clauses may be presented as follows: A class of random variables is called uniformly integrable if:
Related corollaries
The following results apply.Potter or Ceramic Artist Truman Bedell from Rexton, has interests which include ceramics, best property developers in singapore developers in singapore and scrabble. Was especially enthused after visiting Alejandro de Humboldt National Park.
- Definition 1 could be rewritten by taking the limits as
- Clearly , and indeed for all n. However,
- and comparing with definition 1, it is seen that the sequence is not uniformly integrable.
- By using Definition 2 in the above example, it can be seen that the first clause is not satisfied as the s are not bounded in . If is a UI random variable, by splitting
- and bounding each of the two, it can be seen that a uniformly integrable random variable is always bounded in . It can also be shown that any random variable will satisfy clause 2 in Definition 2.
- If any sequence of random variables is dominated by an integrable, non-negative : that is, for all ω and n,
Relevant theorems
- A class of random variables is uniformly integrable if and only if it is relatively compact for the weak topology .
- de la Vallée-Poussin theorem[3]
- The family is uniformly integrable if and only if there exists a non-negative increasing convex function such that
Relation to convergence of random variables
Mining Engineer (Excluding Oil ) Truman from Alma, loves to spend time knotting, largest property developers in singapore developers in singapore and stamp collecting. Recently had a family visit to Urnes Stave Church.
- A sequence converges to in the norm if and only if it converges in measure to 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
- 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.
My blog: http://www.primaboinca.com/view_profile.php?userid=5889534 - 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.
My blog: http://www.primaboinca.com/view_profile.php?userid=5889534 - J. Diestel and J. Uhl (1977). Vector measures, Mathematical Surveys 15, American Mathematical Society, Providence, RI ISBN 978-0-8218-1515-1
- ↑ 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.
My blog: http://www.primaboinca.com/view_profile.php?userid=5889534 - ↑ Dellacherie, C. and Meyer, P.A. (1978). Probabilities and Potential, North-Holland Pub. Co, N. Y. (Chapter II, Theorem T25).
- ↑ Meyer, P.A. (1966). Probability and Potentials, Blaisdell Publishing Co, N. Y. (p.19, Theorem T22).
- ↑ 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.
My blog: http://www.primaboinca.com/view_profile.php?userid=5889534