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The Karlin–Rubin theorem for one-sided tests

Statement

Assume AC. Let E=((X,A),Θ,(Pθ)) have MLR in a real statistic T, let θ0∈Θ, and let 0<α<1. A randomized test is a binary-action probability kernel as in Randomized tests; write ψ(x)=K(x,{1}) for its measurable rejection function. For nonempty, disjoint null and alternative sets, power, size, and level have the conventions in Test size and power: βψ(θ)=Eθψ, size sup⁡θ∈Θ0βψ(θ), and level α when the size is at most α. Here a level-α test is uniformly most powerful (UMP) when it has at least as large a power as every other level-α test at every parameter in the alternative set; if an alternative set is empty, define level by the same null-supremum formula and the pointwise UMP comparison is vacuous.

Choose c+,γ+ and c−,γ− such that Pθ0(T>c+)+γ+Pθ0(T=c+)=α,Pθ0(T<c−)+γ−Pθ0(T=c−)=α. Such choices exist by A randomized threshold attains every test size. Then ϕ+=1{T>c+}+γ+1{T=c+} defines a UMP randomized test of level α for H0:θ≤θ0 against H1:θ>θ0, and ϕ−=1{T<c−}+γ−1{T=c−} defines a UMP randomized test of level α for H0:θ≥θ0 against H1:θ<θ0. All hypotheses are intersected with Θ.

Facts & Assumptions

Given: AC, a dominated experiment with Θ⊆R and MLR in T, θ0∈Θ, and 0<α<1.

[F1]

For every θ1<θ2, there is a nondecreasing Borel function q:R→[0,+∞] such that q(T) equals the extended likelihood ratio pθ2/pθ1 μ-almost everywhere on the union of the density supports, with ratio +∞ where pθ1=0<pθ2. The exceptional null set may depend on the parameter pair. (Families with a monotone likelihood ratio)

[F2]

For a real-valued statistic under any probability law and 0<α<1, an upper or lower threshold with boundary randomization can be chosen to have rejection probability exactly α. (A randomized threshold attains every test size)

[F3]

Under MLR, the rejection probability of an upper randomized threshold is nondecreasing in θ, while that of a lower randomized threshold is nonincreasing. (MLR orders the power of randomized thresholds)

[F4]

A randomized test has a measurable rejection-probability function ψ:X→[0,1]. (Randomized tests)

[F5]

A density pθ defines the measure pθ dμ=Pθ, and for nonnegative measurable g one has Eθg=∫gpθ dμ. (Likelihood densities, The measure with density f relative to μ, Integrating against a density agrees with integrating the product)

[F6]

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))

[F7]

For each parameter the chosen likelihood density is a finite-valued nonnegative representative of its Radon–Nikodym class. (Likelihood densities)

[F8]

For nonempty disjoint null and alternative sets, the power is Eθψ, size is the supremum of power on the null, and level α means size at most α. (Test size and power)

[F9]

Sums, scalar multiples, and products of measurable extended-real functions are measurable whenever the pointwise operations are defined. (Arithmetic and lattice operations preserve measurability whenever they are defined)

[F10]

A probability kernel assigns a probability measure to each x and has a measurable event-evaluation function x↦K(x,B) for each action event B. (Measure kernel and probability kernel)

[A1]

AC permits the simultaneous choice of likelihood-density representatives in Likelihood densities, as required by the MLR definition (The Axiom of Choice).

Proof

1.1givenF2F4F7F9F10A1chooseconstruct

By [F2], choose c+,γ+ and c−,γ− so both threshold functions have expectation α under Pθ0. Since T is measurable, their threshold events and functions ϕ+,ϕ− are measurable; disjointness of each pair of threshold events and γ±∈[0,1] give 0≤ϕ±≤1. For each sign, define K±(x,B)=ϕ±(x)1{1∈B}+(1−ϕ±(x))1{0∈B} for B⊆{0,1}. 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; thus K±(x,⋅) is a probability measure. Each event evaluation is one of 0,1,ϕ±,1−ϕ±, hence measurable by [F9]. Thus [F10] gives probability kernels, which are randomized tests by [F4], with the stated functions. Select the finite-valued density representatives used below as in [F7] and [A1].

2.1step 1.1F3F8algebra

By [F3], Eθϕ+≤Eθ0ϕ+=α for θ≤θ0, and Eθϕ−≤Eθ0ϕ−=α for θ≥θ0. Each null contains θ0, so its supremum size is at most α; by [F8] and the empty-alternative convention in the statement, both candidates have level α for their stated nulls.

2.2givenF1F5F7F9step 1.1

Fix θ1>θ0 with densities p0,p1, let q be their Borel MLR function, and let N be its pairwise μ-null exceptional set from [F1]. By [F5], Pθ0(N)=0 and Pθ0(p0>0)=1. Put k=q(c+). The value k is finite: if Pθ0(T=c+)>0, there is a point of this boundary outside N with p0>0 and finite p1; otherwise Pθ0(T>c+)=α>0 gives such a point above the threshold and monotonicity forces q(c+)<∞. On {p0>0}∖N, p1−kp0=p0(q(T)−k) is nonnegative above c+, nonpositive below it, and zero at T=c+. On {p0=0<p1}∖N, q(T)=+∞; finite k and monotonicity force T>c+, where ϕ+=1. Where both densities vanish, p1−kp0=0. By [F9], the products and differences are measurable, so every competing test ψ satisfies (ϕ+−ψ)(p1−kp0)≥0 μ-almost everywhere.

3.1step 1.1step 2.1step 2.2F5F6F8F9algebra

The measurable product in step 2.2 is integrable because ∣ϕ+−ψ∣≤1 and p0,p1 each integrate to one. Apply [F5] to the positive and negative parts of ϕ+−ψ; linearity [F6] then gives Eθ1(ϕ+−ψ)−kEθ0(ϕ+−ψ)=∫(ϕ+−ψ)(p1−kp0) dμ≥0. For a level-α competitor, [F8] and step 1.1 give Eθ0ψ≤α=Eθ0ϕ+, while k≥0; thus Eθ1ϕ+≥Eθ1ψ. Since θ1>θ0 was arbitrary, ϕ+ is UMP for the upper alternative.

4.1F1step 1.1step 3.1constructalgebra∎

Reparameterize by η=−θ and put S=−T. For η1<η2, let q represent p−η1/p−η2 as in [F1]. Define h(u)=1/u for 0<u<+∞, h(0)=+∞, and h(+∞)=0. The function s↦h(q(−s)) is Borel and nondecreasing, since reflection and h both reverse order. Outside the original pairwise exceptional set on the union of supports, it represents p−η2/p−η1, including points where either density is zero; where both vanish no ratio is required. Thus the reflected family has MLR in S. Apply the upper-tail result of steps 1.1–3.1 at η0=−θ0 with cutoff −c− and boundary probability γ−, calibrated by step 1.1. The resulting UMP test is exactly ϕ− for the original lower alternative.

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