Tuesday, 6 December 2016

probability - Does finite expectation imply bounded random variable?



In general if I have that
E(X)<
does this imply that |X|< a.s? An attempted proof:



Let (Ω,F,P) be a probability space and K>0 be a constant, then

E(X)=ΩXdP={|X|K}XdP+{|X|>K}XdP<
Taking the limit as K
{|X|>K}XdP0
Struggling how to finish from here or posssibly this isn't true? Thanks!


Answer



If not |X|< a.s., then there is a set of positive measure where |X|=. The integral

ΩXdP=Ωmax{0,X}dPΩmax{0,X}dP
is defined only when at most one of the summands is infinite. If X=+ on a set of positive measure, then X= only on a zero-set, and then E(X)=+. Similarly, if X= on a set of positive measure, then E(X)=. Thus we have the following possibilities:




  • |E(X)|< and |X|< a.s.

  • E(X)=+ and X> a.s.

  • E(X)= and X< a.s.

  • X is not integrable (E(X) does not exixts)




(So specifically, as you wrote E(X)< without absolute value, it is possible to have |X|= with positive probability)


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