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The Karlin–Rubin theorem for one-sided tests
Statement
Assume AC. Let have MLR in a real statistic , let , and let . A randomized test is a binary-action probability kernel as in Randomized tests; write for its measurable rejection function. For nonempty, disjoint null and alternative sets, power, size, and level have the conventions in Test size and power: , size , and level when the size is at most . Here a level- test is uniformly most powerful (UMP) when it has at least as large a power as every other level- 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 and such that Such choices exist by A randomized threshold attains every test size. Then defines a UMP randomized test of level for against , and defines a UMP randomized test of level for against . All hypotheses are intersected with .
Facts & Assumptions
Given: AC, a dominated experiment with and MLR in , , and .
For every , there is a nondecreasing Borel function such that equals the extended likelihood ratio -almost everywhere on the union of the density supports, with ratio where . The exceptional null set may depend on the parameter pair. (Families with a monotone likelihood ratio)
For a real-valued statistic under any probability law and , an upper or lower threshold with boundary randomization can be chosen to have rejection probability exactly . (A randomized threshold attains every test size)
Under MLR, the rejection probability of an upper randomized threshold is nondecreasing in , while that of a lower randomized threshold is nonincreasing. (MLR orders the power of randomized thresholds)
A randomized test has a measurable rejection-probability function . (Randomized tests)
A density defines the measure , and for nonnegative measurable one has . (Likelihood densities, The measure with density relative to , Integrating against a density agrees with integrating the product)
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 )
For each parameter the chosen likelihood density is a finite-valued nonnegative representative of its Radon–Nikodym class. (Likelihood densities)
For nonempty disjoint null and alternative sets, the power is , size is the supremum of power on the null, and level means size at most . (Test size and power)
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)
A probability kernel assigns a probability measure to each and has a measurable event-evaluation function for each action event . (Measure kernel and probability kernel)
AC permits the simultaneous choice of likelihood-density representatives in Likelihood densities, as required by the MLR definition (The Axiom of Choice).
Proof
By [F2], choose and so both threshold functions have expectation under . Since is measurable, their threshold events and functions are measurable; disjointness of each pair of threshold events and give . For each sign, define for . 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 is a probability measure. Each event evaluation is one of , 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].
By [F3], for , and for . Each null contains , so its supremum size is at most ; by [F8] and the empty-alternative convention in the statement, both candidates have level for their stated nulls.
Fix with densities , let be their Borel MLR function, and let be its pairwise -null exceptional set from [F1]. By [F5], and . Put . The value is finite: if , there is a point of this boundary outside with and finite ; otherwise gives such a point above the threshold and monotonicity forces . On , is nonnegative above , nonpositive below it, and zero at . On , ; finite and monotonicity force , where . Where both densities vanish, . By [F9], the products and differences are measurable, so every competing test satisfies -almost everywhere.
The measurable product in step 2.2 is integrable because and each integrate to one. Apply [F5] to the positive and negative parts of ; linearity [F6] then gives . For a level- competitor, [F8] and step 1.1 give , while ; thus . Since was arbitrary, is UMP for the upper alternative.
Reparameterize by and put . For , let represent as in [F1]. Define for , , and . The function is Borel and nondecreasing, since reflection and both reverse order. Outside the original pairwise exceptional set on the union of supports, it represents , including points where either density is zero; where both vanish no ratio is required. Thus the reflected family has MLR in . Apply the upper-tail result of steps 1.1–3.1 at with cutoff and boundary probability , calibrated by step 1.1. The resulting UMP test is exactly for the original lower alternative.
Depends on
- Families with a monotone likelihood ratio
- A randomized threshold attains every test size
- MLR orders the power of randomized thresholds
- Randomized tests
- Measure kernel and probability kernel
- Test size and power
- Likelihood densities
- The measure with density $f$ relative to $\mu$
- Integrating against a density agrees with integrating the product
- Integrable real and complex functions, and their integrals
- Monotonicity and nonnegative homogeneity of the nonnegative integral
- The Lebesgue integral is linear on $L^1(\mu)$
- Arithmetic and lattice operations preserve measurability whenever they are defined
- The Axiom of Choice
Used by
Dependency tree · two levels
55 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- George Deligiannidis, Foundations of Statistical Inference, Theorem 13.5 and proof, PDF pp. 77–78 (standard reference, not scraped)
- Jun Shao, Stat 610 Mathematical Statistics, Lecture 11, Theorem 8.3.17 and proof, PDF pp. 8–10 (standard reference, not scraped)
- Karl B. Gregory, STAT 713 Lecture 11, Karlin–Rubin theorem and examples, annotated PDF pp. 13–22 (standard reference, not scraped)