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A two-sided normal alternative need not have a UMP test

Statement

Assume AC. Let X have law Pθ=N(θ,1) on R, with θ∈R, and test the simple null H0:θ=0 against the two-sided alternative H1:θ∈{−1,1}. A randomized test is a binary-action probability kernel as in Randomized tests; write ψ for its measurable rejection function. Level α and power use Test size and power. Here UMP means maximizing power at each alternative among level-α tests. For every 0<α<1, no such test maximizes power at both alternative values.

Facts & Assumptions

Given: AC and 0<α<1.

[A1]

AC states that every family of nonempty sets has a choice function. (The Axiom of Choice)

[F1]

N(0,1) has density φ(x)=e−x2/2/2π, and N(θ,1) is the law of x↦θ+x under N(0,1). (Standard normal and normal laws)

[F2]

Translation preserves Lebesgue measurability and Lebesgue measure. (Lebesgue outer measure, Lebesgue measurability and Lebesgue measure are unchanged by translation)

[F3]

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)

[F4]

The measure with density f is (f dλ)(A)=∫Af dλ, and integration against it is integration of the product against λ. (The measure with density f relative to μ, Integrating against a density agrees with integrating the product)

[F5]

Countable choice selects from every sequence of nonempty sets; under that principle every singleton in R is Lebesgue null. (The Axiom of Countable Choice (ACω), A box with a degenerate side is Lebesgue null, and so is every coordinate hyperplane in Rn)

[F7]

Integrability is ∫∣f∣<∞, the nonnegative integral is monotone, and the Lebesgue integral is linear on L1. (Integrable real and complex functions, and their integrals, Monotonicity and nonnegative homogeneity of the nonnegative integral, The Lebesgue integral is linear on L1(μ))

[F8]

A randomized test is a binary-action probability kernel; its test function is a measurable rejection-probability function with values in [0,1]. (Randomized tests)

[F9]

For any probability law and 0<α<1, an upper or lower threshold with boundary randomization can attain rejection probability α. (A randomized threshold attains every test size)

[F10]

For a nonempty simple null and alternative, power is rejection probability at each parameter and level α means null rejection probability at most α. (Test size and power)

[F11]

A nonnegative measurable function has integral zero exactly when it vanishes almost everywhere. (A nonnegative measurable function has integral 0 exactly when it vanishes almost everywhere)

[F12]

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)

[F13]

The Borel sigma-algebra on R contains every open ray, so the threshold indicators are measurable. (The Borel sigma-algebra of a topological space)

[F14]

A probability kernel assigns a probability measure to each observation and has measurable event-evaluation functions. (Measure kernel and probability kernel)

Proof

1.1A1F1F2F3F12given

Write φ(x)=e−x2/2/2π and Pθ=N(θ,1). The translation τθ(x)=x+θ is measurable and preserves Lebesgue measure by [F2]–[F3]; [F1] makes Pθ the push-forward of φ dλ under τθ. For each Borel A, x↦φ(x−θ) is continuous, so its product with the measurable indicator 1A is measurable by [F12]; integral invariance gives Pθ(A)=∫Aφ(x−θ) dλ(x), so pθ(x)=φ(x−θ) is a positive density for every θ.

2.1A1F4F5F8F9F10F12F13F14step 1.1chooseconstruct

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 λ-null. By step 1.1 and [F4], P0({c})=∫{c}p0 dλ=0 for every real c. Applying [F9] under P0 gives finite c+,c− with P0(X>c+)=P0(X<c−)=α, since the boundary masses vanish. The threshold indicators are measurable by [F13]. For B⊆{0,1}, define K±(x,B)=ϕ±(x)1{1∈B}+(1−ϕ±(x))1{0∈B}. 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 ϕ±, which are randomized tests by [F8] and have level α by [F10].

3.1step 1.1step 2.1F4F5F7F10F11F12algebra

Step 1.1 gives p1(x)/p0(x)=ex−1/2, strictly increasing, and p−1(x)/p0(x)=e−x−1/2, strictly decreasing. Put k+=ec+−1/2>0. For any level-α test ψ, (ϕ+−ψ)(p1−k+p0)≥0: above c+ both factors are nonnegative, below it both are nonpositive, and at c+ the second vanishes. [F12] makes this product measurable; since ∣ϕ+−ψ∣≤1 and each density integrates to one, [F7] makes it integrable. Apply [F4] to the positive and negative parts of ϕ+−ψ, then [F7] gives E1(ϕ+−ψ)−k+E0(ϕ+−ψ)=∫(ϕ+−ψ)(p1−k+p0) dλ≥0. By [F10] and step 2.1, E0ϕ+=α≥E0ψ, so E1ϕ+≥E1ψ. If equality holds, the two nonnegative terms in E1(ϕ+−ψ)=∫(ϕ+−ψ)(p1−k+p0) dλ+k+E0(ϕ+−ψ) vanish. [F11] makes the integrand zero λ-almost everywhere; strict monotonicity then forces ψ=ϕ+ off c+ almost everywhere, and step 2.1 gives P0({c+})=0. Since P0 has density p0, equality forces ψ=ϕ+ P0-almost surely, so ϕ+ is most powerful against θ=1, uniquely up to P0-almost-sure equality of rejection functions.

3.2step 1.1step 2.1F4F5F7F10F11F12algebra

Put k−=e−c−−1/2>0. For x<c−, ϕ−(x)−ψ(x)≥0 and p−1(x)−k−p0(x)>0; for x>c− both are nonpositive, and at c− 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 ϕ−−ψ, then [F7] gives E−1(ϕ−−ψ)−k−E0(ϕ−−ψ)=∫(ϕ−−ψ)(p−1−k−p0) dλ≥0. By [F10] and step 2.1, E0ϕ−=α≥E0ψ, so E−1ϕ−≥E−1ψ. If equality holds, both nonnegative terms in the sum vanish; [F11] makes the integrand zero λ-almost everywhere, and strict decrease of p−1/p0 forces ψ=ϕ− off c− almost everywhere. Step 2.1 gives P0({c−})=0, and its density then gives equality P0-almost surely, proving uniqueness up to P0-almost-sure equality of rejection functions against θ=−1.

4.1step 2.1step 3.1step 3.2F1F4F5F6F7F10algebra∎

If a UMP test ψ∗ existed, its optimality at θ=1 and step 3.1 would force ψ∗=ϕ+ P0-almost surely, while its optimality at θ=−1 and step 3.2 would force ψ∗=ϕ− P0-almost surely. Choose x0>max⁡(c+,c−); positivity and continuity of p0 give a nondegenerate interval above both cutoffs on which p0 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 P0 measure by [F4] and [F7], but ϕ+=1 and ϕ−=0 there, contradicting the two almost-sure equalities. Therefore no UMP level-α test exists.

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