Let $\xi, \eta$ be a discrete random values and $\mathbb E| ξ |$, $\mathbb E | η | < +\infty$, and any value of these values are accepted with a non-zero probability. How to prove that from $\mathbb E (ξ \mid η) ≥ η$, $\mathbb E (η \mid ξ) ≥ ξ$ follows $ξ = η$?
1 Answer
Let us prove the desired conclusion generally, without assuming that the random variables $\xi$ and $\eta$ are discrete. Let $g\colon\mathbb R\to\mathbb R$ be any strictly increasing strictly convex differentiable function such that $|g(x)|\le1+|x|$ for all real $x$, so that $|g'|\le1$ and $Eg(\xi),Eg(\eta)$ exist in $\mathbb R$. (For instance, one may take $g(x):=E(x+Z)_+=\int_{-x}^\infty(x+z)\varphi(z)\,dz$, so that $0<g(x)\le|x|+EZ_+\le|x|+1$ and $g'(x)=\int_{-x}^\infty\varphi(z)\,dz=\int_{-\infty}^x \varphi(z)\,dz$, where $Z\sim N(0,1)$ and $\varphi$ is the pdf of $Z$.)
By the convexity of $g$, \begin{equation} g(\xi)\ge g(E_\eta\xi)+g'(E_\eta\xi)(\xi-E_\eta\xi), \tag{1} \end{equation} where $E_\eta\xi:=E(\xi|\eta)$. Moreover, by the strict convexity of $g$, inequality (1) is strict on the event $\{\xi\ne E_\eta\xi\}$. Taking the expectations of both sides of (1), we have \begin{equation} Eg(\xi)\ge Eg(E_\eta\xi)+EE_\eta g'(E_\eta\xi)(\xi-E_\eta\xi) =Eg(E_\eta\xi)+Eg'(E_\eta\xi)E_\eta (\xi-E_\eta\xi) =Eg(E_\eta\xi), \end{equation} so that we have the conditional Jensen inequality \begin{equation} Eg(\xi)\ge Eg(E_\eta\xi), \tag{2} \end{equation} and this inequality is strict unless $\xi=E_\eta\xi$ almost surely (a.s.). Similarly, \begin{equation} Eg(\eta)\ge Eg(E_\xi\eta), \tag{3} \end{equation} and this inequality is strict unless $\eta=E_\xi\eta$ a.s.
Using now (2), the conditions $E_\eta\xi\ge\eta$ and $E_\xi\eta\ge\xi$ given in the OP, the condition that $g$ is increasing, and (3), we have \begin{equation} Eg(\xi)\ge Eg(E_\eta\xi)\ge Eg(\eta)\ge Eg(E_\xi\eta)\ge Eg(\xi), \tag{4} \end{equation} and (since $g$ is strictly increasing) the 2nd and 4th inequalities here are strict unless, respectively, $E_\eta\xi=\eta$ a.s. and $E_\xi\eta=\xi$ a.s. However, all the inequalities in (4) must be the equalities. It follows that $\xi=E_\eta\xi=\eta$ a.s., whence $\xi=\eta$ a.s., as desired.
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1$\begingroup$ I have now given a very different proof, which holds in complete generality, without assuming that $\xi$ and $\eta$ are discrete. $\endgroup$ Commented Dec 12, 2018 at 6:15
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$\begingroup$ As @Iosif mentions, the argument applies with any $g$. One can take $g(x) = x$. $\endgroup$ Commented Dec 16, 2018 at 18:06
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$\begingroup$ @VictorZurkowski : $g(x)\equiv x$ would not quite do. As specified in the answer, $g$ has to be strictly convex -- which is then used to get the strict inequality in (2) (unless $\xi=E_\eta\xi$ a.s. $\endgroup$ Commented Dec 16, 2018 at 22:16
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$\begingroup$ '@Iosif, $E(\eta) \le E(E(\xi| \eta) ) = E(\xi) \le E( E(\eta|\xi)) = E(\eta) $, therefore the inequalities are equalities. Then $E(\xi|\eta) - \eta$ is a non-negative function whose integral is 0, hence $E(\xi|\eta) = \eta$ a.e.; likewise $ E(\eta|\xi)) - \xi$ is a non-negative function whose integral is 0, so $ E(\eta|\xi)) = \xi$ a.e. $\endgroup$ Commented Dec 16, 2018 at 22:29
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1$\begingroup$ @VictorZurkowski : So, you have $E_\eta\xi=\eta$ and $E_\xi\eta=\xi$. How do you get $\xi=\eta$ from here? I think the strict version of the conditional Jensen inequality is essential here. $\endgroup$ Commented Dec 17, 2018 at 2:27