# Conditional expectation of two identical marginal normal random variables

Let $Y_0$ and $Y_1$ be both identically (not necessarily independent) normally distributed with mean $\mu$ and $\sigma^2$, i.e., $Y_i \sim N(\mu, \sigma^2)$ for $i = 1, 2$. Let $\rho$ denote the correlation between $Y_0$ and $Y_1$. Show that the conditional expectation $E[Y_1|Y_0 = y_0] = (1-\rho)\mu + \rho y_0$.

I know that in general if $(X, Y)$ are bivariate normally distributed, then $E[X|Y] = E[X] + \rho \frac{\sigma_X}{\sigma_Y}(Y-E[Y])$. But in this question, there is no indication that $Y_0$ and $Y_1$ are jointly normally distributed; they are simply marginally normally distributed. If they were joint normal, the result falls out simply from the result I just mentioned. Any ideas on how I can prove the result?

• Without joint normality you could look into copulas – kjetil b halvorsen Oct 19 '17 at 9:26
• Any ideas on how one would go about doing that? Is it even possible to find an expression for the conditional expectation without the assumption of joint normality? I'm beginning to think there's a mistake in the question. – elbarto Oct 19 '17 at 9:28
• The second bivariate distribution plotted here stats.stackexchange.com/questions/176411/… and the bivariate distribution of $Y$ and $Z$ discussed here stats.stackexchange.com/questions/125648/… and the two examples here stats.stackexchange.com/questions/120861/… are all examples of what you ask about. – Glen_b Jul 13 '18 at 9:53
• @Xi'an I believe there are bivariate distributions with Normal marginals, (nonzero) linear conditional expectation function, but which are not bivariate Normal. All these will correspond to copulas that produce linear conditional expectations. To produce such a counterexample, it suffices to produce one with zero conditional expectation and standard Normal marginals--you can then scale and mix it with any bivariate Normal distribution. – whuber Jul 15 '18 at 13:45
• @whuber: you are correct, most obviously! – Xi'an Jul 15 '18 at 17:39

I don't think the formula given in the question can be correct in all cases, it is developed using joint normality. Without joint normality we can use copulas. For $X,Y$ random variables with joint distribution with cumulative distribution function $F(x,y)$ and joint density $f(x,y)$ define the transformed random variables (rv) $U=F_X(X), V=F_Y(Y)$ where $F_X, F_Y$ denotes the marginal cumulative distribution functions (cdf). Then the joint distribution of $U;V$ $$\DeclareMathOperator{\P}{\mathbb{P}} \P(U \le u, V \le v)=C(u,v)$$ is the copula of $X$ and $Y$, with copula density $c(u,v)$ (when it exists). So, in your setup let us assume that the copula density exists, and for simplicity I will take both $X$ and $Y$ as standard normals. So what possibility exists for the conditional expectation of $X$ given $Y=y$ ? Using Sklar's theorem we can write the joint density as $$f(x,y) = c(\Phi(x),\Phi(y)) \phi(x) \phi(y)$$ where $\Phi, \phi$ are the standard normal cdf, pdf, respectively. Then the conditional density is given by $$f(x \mid y) = \frac{f(x,y)}{f(y)}=\frac{c(\Phi(x),\Phi(y))\phi(x)\phi(y)}{\phi(y)}= c(\Phi(x),\Phi(y))\phi(x)$$ Then we can look at this with various copula functions, see https://en.wikipedia.org/wiki/Copula_(probability_theory) .

There is a general inequality for copulas $$W(u,v)=\max(u+v-1,0) \le C(u,v) \le M(u,w)=\min(u,v)$$ where both upper and lower limits are copulas (This is the Frechet-Hoeffding bounds). The upper limit isn't very interesting, since it gives $\P(U=V)=1$ so gives correlation equal 1. The lower limit similarly corresponds to $U=1-V$ with probability one, so correlation is -1. But for these two extremal copula the conditional expectation function certainly is linear!

Lets look at some intermediate cases. I will use the R package copula and some numerical integration to find the conditional expectation function, for the case of the gumbel copula. The code can be simply adapted for other copulas.

library(copula)
C  <-  gumbelCopula(2)
make_cond <-  Vectorize(function(y) {
function(x) dCopula( cbind(pnorm(x), pnorm(y)), C)*dnorm(x)
}  )


the last command makes a function representing the conditional density given $Y=y$. Let us look at how this looks like for three different values of $y$:

cond_dens <- make_cond(c(-1, 0, 1))
plot(cond_dens[[1]],  from=-3,  to=3, col="blue", ylim=c(0, 0.7))

showing clearly that the conditional distributions now are non-normal. We can also see clearly that the conditional variance is non-constant. For more examples using the copula package see https://www.r-bloggers.com/modelling-dependence-with-copulas-in-r/ and Generating values from copula using copula package in R . Then we can find the conditional expectation function using numerical integration:
plot(function(y) sapply(make_cond(y), FUN=function(fun)
lower=-Inf,  upper=Inf)$value), from=-3, to=3, ylab="conditional expectation given y", xlab="y") title("conditional expectation of X given Y=y")  and it is quite clear that the conditional expectation function is not linear! Here is an example using the Ali-Mikhail-Haq copula: with cdf $$F_\theta(x,y)=\dfrac{xy}{1-\theta(1-x)(1-y)}\qquad\theta\in(-1,1)$$ and associated joint density $$f_\theta(x,y)=\dfrac{1+\theta[(1+x)(1+y)-3]+\theta^2(1-x)(1-y)) }{[1-\theta(1-x)(1-y)]^3}$$ Simulating a sample from the conditional distribution associated with this joint distribution (by Metropolis-Hastings) and applying the Normal quantile function transform to the sample returns the following graph of$\mathbb{E}[X|Y]$for$\theta=0.5, -0.9$, clearly not a linear regression: • Xi'an, if you think this is clear enough to serve as an answer to the duplicate, please let us know and we can merge these two threads. Otherwise I (at least) am happy to let this thread, although closed, to stay as-is. (+1 btw.) – whuber Jul 16 '18 at 13:23 Consider$X$and$Y$with joint pdf $$f_{X,Y}(x,y) = \begin{cases}2\phi(x)\phi(y), & x, y \geq 0 ~~ \text{or}~~ x, y < 0,\\ 0, & \text{otherwise.}\end{cases}$$ It is readily verified that$X$and$Y$are marginally standard normal random variables, but they are not jointly normal random variables, and they are not independent random variables; their correlation coefficient$\rho$is positive and the linear minimum mean-square error (MMSE) estimate of$Y$given that$X=x$is just$\rho x$, a straight line with slope$\rho$passing through the origin. On the other hand, for$x \geq 0$, the conditional density of$Y$given that$X=xis $$f_{Y\mid X}(y\mid X=x) = \frac{f_{X,Y}(x,y)}{f_X(x)} = 2\phi(y)\mathbf 1_{y\geq 0}$$ and so \begin{align} E[Y\mid X = x] &= \int_0^\infty y \cdot 2\phi(y) \,\mathrm dy = \sqrt{\frac{2}{\pi}}. \end{align} Similarly, forx<0$,$E[Y \mid X=x] = -\sqrt{\frac{2}{\pi}}$, that is, regarded as a function of$x$,$E[Y \mid X=x]$is a piecewise constant function (or step function) that jumps abruptly at$x=0$from value$-\sqrt{\frac{2}{\pi}}$on the negative axis to value$+\sqrt{\frac{2}{\pi}}$on the positive axis. This is quite different from the linear MMSE estimate of$\rho x$. Indeed, the two estimates are equal only at two points in the first and third quadrants respectively. In short, I don't believe that the result that the OP wishes to prove is correct. Of course, as the OP says, if$X$and$Y$are jointly normal, then the result is equivalent to the fact that for jointly normal random variables, the linear MMSE estimator is the same as the MMSE estimator$E[Y\mid X=x]\$.