Law of total covariance
In probability theory, the law of total covariance, covariance decomposition formula, or ECCE states that if X, Y, and Z are random variables on the same probability space, and the covariance of X and Y is finite, then
(The conditional expected values E( X | Z ) and E( Y | Z ) are random variables in their own right, whose values depends on the value of Z. Notice that the conditional expected value of X given the event Z = z is a function of z (this is where adherence to the conventional rigidly case-sensitive notation of probability theory becomes important!). If we write E( X | Z = z) = g(z) then the random variable E( X | Z ) is just g(Z). Similar comments apply to the conditional covariance.)
The law of total covariance can be proved using the law of total expectation: First,
from the definition of covariance. Then we apply the law of total expectation by conditioning on the random variable Z:
Now we rewrite the term inside the first expectation using the definition of covariance:
Since expectation of a sum is the sum of expectations, we can regroup the terms:
Finally, we recognize the final two terms as the covariance of the conditional expectations E[X|Z] and E[Y|Z]:
Notes and references
- Matthew R. Rudary, On Predictive Linear Gaussian Models, ProQuest, 2009, page 121.
- Sheldon M. Ross, A First Course in Probability, sixth edition, Prentice Hall, 2002, page 392.
- Law of total variance, a special case corresponding to X = Y.