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A two-sided normal alternative need not have a UMP test
Statement
Assume AC. Let have law on , with , and test the simple null against the two-sided alternative . A randomized test is a binary-action probability kernel as in Randomized tests; write for its measurable rejection function. Level and power use Test size and power. Here UMP means maximizing power at each alternative among level- tests. For every , no such test maximizes power at both alternative values.
Facts & Assumptions
Given: AC and .
AC states that every family of nonempty sets has a choice function. (The Axiom of Choice)
has density , and is the law of under . (Standard normal and normal laws)
Translation preserves Lebesgue measurability and Lebesgue measure. (Lebesgue outer measure, Lebesgue measurability and Lebesgue measure are unchanged by translation)
A measurable self-map that preserves the measure on every measurable set preserves integrals of nonnegative measurable functions. (Measure-preserving transformations and systems, Integral invariance under measure-preserving maps)
The measure with density is , and integration against it is integration of the product against . (The measure with density relative to , Integrating against a density agrees with integrating the product)
Countable choice selects from every sequence of nonempty sets; under that principle every singleton in is Lebesgue null. (The Axiom of Countable Choice (), A box with a degenerate side is Lebesgue null, and so is every coordinate hyperplane in )
Under countable choice, a nondegenerate interval has its positive length as Lebesgue measure. (A box in with parameters is Lebesgue measurable of measure , whichever of its faces are included)
Integrability is , the nonnegative integral is monotone, and the Lebesgue integral is linear on . (Integrable real and complex functions, and their integrals, Monotonicity and nonnegative homogeneity of the nonnegative integral, The Lebesgue integral is linear on )
A randomized test is a binary-action probability kernel; its test function is a measurable rejection-probability function with values in . (Randomized tests)
For any probability law and , an upper or lower threshold with boundary randomization can attain rejection probability . (A randomized threshold attains every test size)
For a nonempty simple null and alternative, power is rejection probability at each parameter and level means null rejection probability at most . (Test size and power)
A nonnegative measurable function has integral zero exactly when it vanishes almost everywhere. (A nonnegative measurable function has integral exactly when it vanishes almost everywhere)
Sums, products, and scalar multiples of measurable functions are measurable whenever the pointwise operations are defined. (Arithmetic and lattice operations preserve measurability whenever they are defined)
The Borel sigma-algebra on contains every open ray, so the threshold indicators are measurable. (The Borel sigma-algebra of a topological space)
A probability kernel assigns a probability measure to each observation and has measurable event-evaluation functions. (Measure kernel and probability kernel)
Proof
Write and . The translation is measurable and preserves Lebesgue measure by [F2]–[F3]; [F1] makes the push-forward of under . For each Borel , is continuous, so its product with the measurable indicator is measurable by [F12]; integral invariance gives , so is a positive density for every .
AC [A1] applied to any sequence of nonempty sets supplies a choice function on its values and hence a sequence choice, so [F5] shows every singleton is -null. By step 1.1 and [F4], for every real . Applying [F9] under gives finite with , since the boundary masses vanish. The threshold indicators are measurable by [F13]. For , define . The two weights are nonnegative and sum to one, and the formula is countably additive because each point belongs to at most one event in a disjoint sequence; each event evaluation is measurable by [F12]–[F13]. Thus [F14] gives probability kernels with test functions , which are randomized tests by [F8] and have level by [F10].
Step 1.1 gives , strictly increasing, and , strictly decreasing. Put . For any level- test , : above both factors are nonnegative, below it both are nonpositive, and at the second vanishes. [F12] makes this product measurable; since and each density integrates to one, [F7] makes it integrable. Apply [F4] to the positive and negative parts of , then [F7] gives . By [F10] and step 2.1, , so . If equality holds, the two nonnegative terms in vanish. [F11] makes the integrand zero -almost everywhere; strict monotonicity then forces off almost everywhere, and step 2.1 gives . Since has density , equality forces -almost surely, so is most powerful against , uniquely up to -almost-sure equality of rejection functions.
Put . For , and ; for both are nonpositive, and at the density difference is zero. By [F12] the product is measurable; it is nonnegative and integrable by [F7]. Apply [F4] to the positive and negative parts of , then [F7] gives . By [F10] and step 2.1, , so . If equality holds, both nonnegative terms in the sum vanish; [F11] makes the integrand zero -almost everywhere, and strict decrease of forces off almost everywhere. Step 2.1 gives , and its density then gives equality -almost surely, proving uniqueness up to -almost-sure equality of rejection functions against .
If a UMP test existed, its optimality at and step 3.1 would force -almost surely, while its optimality at and step 3.2 would force -almost surely. Choose ; positivity and continuity of give a nondegenerate interval above both cutoffs on which is bounded below by a positive constant. By [F6] and the countable choice in step 2.1, that interval has positive Lebesgue and hence positive measure by [F4] and [F7], but and there, contradicting the two almost-sure equalities. Therefore no UMP level- test exists.
Depends on
- The Axiom of Choice
- Standard normal and normal laws
- Lebesgue outer measure, Lebesgue measurability and Lebesgue measure are unchanged by translation
- Measure-preserving transformations and systems
- Integral invariance under measure-preserving maps
- The measure with density $f$ relative to $\mu$
- Integrating against a density agrees with integrating the product
- Measure kernel and probability kernel
- Integrable real and complex functions, and their integrals
- Monotonicity and nonnegative homogeneity of the nonnegative integral
- The Lebesgue integral is linear on $L^1(\mu)$
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- A box with a degenerate side is Lebesgue null, and so is every coordinate hyperplane in $\mathbb{R}^n$
- A box in $\mathbb{R}^n$ with parameters $a_i\le b_i$ is Lebesgue measurable of measure $\prod_{i<n}(b_i-a_i)$, whichever of its faces are included
- A randomized threshold attains every test size
- Randomized tests
- Test size and power
- A nonnegative measurable function has integral $0$ exactly when it vanishes almost everywhere
- Arithmetic and lattice operations preserve measurability whenever they are defined
- The Borel sigma-algebra of a topological space
Used by
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Sources
- George Deligiannidis, Foundations of Statistical Inference, Neyman–Pearson Theorem 13.2 and UMP discussion, PDF pp. 77–78 (standard reference, not scraped)
- Karl B. Gregory, STAT 713 Lecture 11, Neyman–Pearson lemma and proof annotations, annotated PDF pp. 6–9 (standard reference, not scraped)