Some Statistical Decision Problems Much of the theory of statistical inference (the subject of Chapters 6-10 of this text) deals with problems in which one has to make one of several available choices. Generally, which choice is best depends on some random variable that has not yet been observed. One example was already discussed in Section 4.5, where we introduced the mean squared error (M.S.E_) and mean absolute error (M.A,l?l,) criteria for predicting a random
variable. In these cases we have to choose a number d for our prediction of a random variable Y. Which prediction will be best depends on the value of Y that we do not yet know. Random variables like -’ - dl and -(Y - d)2 are gambles, and the choice of gamble that minimizes M.A.E. or M.S.E. is the choice that maximizes an expected utility,