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422    8. Tests of Hypotheses

                                 normal, and gamma, the family of pmf or pdf of the associated sufficient
                                 statistic T would be MLR in T. We leave out the verifications as exercises.

                                      Some families of distributions may not enjoy the MLR property.
                                                  The Exercise 8.4.6 gives an example.

                                 8.4.3   UMP Test via MLR Property


                                 The following useful result is due to Karlin and Rubin (1956). We simply state
                                 the result without giving its proof.
                                    Theorem 8.4.1 (Karlin-Rubin Theorem)  Suppose that we wish to
                                 test H  : θ = θ  versus H  : θ > θ . Consider a real valued sufficient statistic
                                                      1
                                      0
                                             0
                                                            0
                                 T = T(X) for θ ∈ Θ (⊆ ℜ). Suppose that the family {g(t; θ) : θ ∈ Θ} of the
                                 pdf’s induced by T has the MLR (non-decreasing) property. Then, the test
                                 function



                                 corresponds to the UMP level a test if k is so chosen that

                                      If the null hypothesis in the Karlin-Rubin Theorem is replaced by
                                      H : θ ≤ θ , the test given by (8.4.6) continues to be UMP level α.
                                       0     0
                                    Remark 8.4.2 Suppose that the null hypothesis in Theorem 8.4.1 is re-
                                 placed by  H  :  θ ≤  θ . Recall that the level of the test is defined by
                                            0        0
                                                           One may, however, note that the maximum Type
                                 I error probability is attained at the boundary point θ = θ  because of the MLR
                                                                                0
                                 (non-decreasing) property in T. If the distribution of T happens to be discrete,
                                 then the theorem continues to hold but it will become necessary to randomize
                                 on the set {T = k} as we did in the Examples 8.3.6-8.3.7. In the Karlin-Rubin
                                 Theorem, if the family {f(x; θ) : θ ∈ Θ} has instead the MLR non-increasing
                                 property in T(X), then the UMP level α test function ψ(X) would instead be 1
                                 or 0 according as T(X) < k or T(X) > k respectively.
                                    Example 8.4.8 (Example 8.4.1 Continued) Suppose that X , ..., X  are
                                                                                       1
                                                                                              n
                                 iid N(µ, σ ) with unknown µ ∈ ℜ, but assume that σ ∈ ℜ +  is known. Let
                                          2
                                                which is sufficient for µ and its pdf has the MLR increas-
                                 ing property in T. One may use the representation in (8.4.5) and the remark
                                 about the one-parameter exponential family. Now, we wish to test the null
                                 hypothesis H  : µ ≤ µ  versus H  : µ > µ  with level α where µ  is a fixed
                                             0      0        1      0                   0
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