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                            142                   Parametric Families of Distributions

                            Some distribution theory for exponential families
                            The importance of exponential family distributions lies in the way in which the parameter
                            of the model interacts with the argument of the density or frequency function in the model
                            function. For instance, if p(y; θ)isof the form (5.1) and θ 0 and θ 1 are two elements of the
                            parameter space, then log[p(y; θ 1 )/p(y; θ 0 )] is a linear function of T (y) with coefficients
                            depending on θ 0 ,θ 1 :
                                             p(y; θ 1 )                         T
                                          log       = A(θ 0 ) − A(θ 1 ) + [c(θ 1 ) − c(θ 0 )] T (y).
                                             p(y; θ 0 )
                            This type of structure simplifies certain aspects of the distribution theory of the model,
                            particularly those aspects concerned with how the distributions change under changes in
                            parameter values. The following lemma gives a relationship between expectations under
                            two different parameter values.

                            Lemma 5.2. Let Y denote a random variable with model function of the form
                                                       T
                                                  exp{η T (y) − k(η)}h(y), y ∈ Y,
                            where η ∈ H.
                              Fix η 0 ∈ H and let g : Y → R. Then
                                                                                T
                                      E[g(Y); η] = exp{k(η 0 ) − k(η)}E[g(Y)exp{(η − η 0 ) T (Y)}; η 0 ]
                            for any η ∈ H such that
                                                        E[|g(Y)|; η] < ∞.

                            Proof. The proof is given for the case in which Y has an absolutely continuous distribution.
                            Suppose E[|g(Y)|; η] < ∞; then the integral

                                                           g(y)p(y; η) dy
                                                          Y
                            exists and is finite. Note that
                                                          p(y; η)                  p(Y; η)

                                    g(y)p(y; η) dy =  g(y)      p(y; η 0 ) dy = E g(Y)   ; η 0 ;
                                   Y                Y    p(y; η 0 )               p(Y; η 0 )
                              The result now follows from the fact that

                                            p(Y; η)                           T
                                                   = exp{k(η 0 ) − k(η)} exp{(η − η 0 ) T (Y)}.
                                           p(Y; η 0 )

                              Consider a random variable Y with model function of the form
                                                           T
                                                    exp{c(θ) T (y) − A(θ)}h(y);
                            this function can be written as the product of two terms, the term given by the exponential
                            function and h(y). Note that only the first of these terms depends on θ and that term depends
                            on y only through T (y). This suggests that, in some sense, the dependence of the distribution
                            of Y on θ is primarily through the dependence of the distribution of T (Y)on θ. The following
                            two theorems give some formal expressions of this idea.
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