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A countable mixture dominates a sigma-finite dominated family
Statement
Assume AC. Let be a nonempty family of probability measures dominated by a -finite positive measure on . There are a finite or countable sequence from and weights summing to one such that satisfies for every . Repetitions are permitted if is finite.
Facts & Assumptions
Given: AC, a nonempty dominated family, and a sigma-finite dominating measure.
The Axiom of Choice: AC selects RN versions for the possibly uncountable family and countably many finite-subfamily approximations to a supremum.
Dominated statistical experiments: every is absolutely continuous with respect to the supplied sigma-finite .
Finite, sigma-finite, and semifinite measures: has a countable cover by measurable sets of finite measure.
Likelihood densities and A sigma-finite signed measure that is absolutely continuous with respect to a sigma-finite positive measure has a unique almost-everywhere density: each has a nonnegative density with .
Monotone convergence for the integral: increasing nonnegative functions, including indicators and partial sums of densities, pass to their integrals.
Proof
Choose a countable measurable partition of with by disjointifying a sigma-finite cover [F2]. Put and . For disjoint measurable , countable additivity of and interchange of nonnegative double series give ; the interchange is valid because both sides are the supremum of the same finite subsums. Thus is a measure. Also . Since every , iff every , iff . Thus is finite and equivalent to , so every .
For each , choose the RN density from [F3], finite outside a -null set, and let . Fix ; since , . Define , so . By [A1], choose finite for each with union measure greater than . For all sufficiently large , these unions have positive measure, so the selected index set is nonempty. Set . Finite unions from the selected sets have measure at most , so [F4] applied to their indicators gives by continuity from below; the approximations give . Hence .
Fix . If , the increasing finite unions converge to , whose measure is greater than . [F4] applied to their indicators shows that some finite union then exceeds , contradicting its definition. Therefore -almost everywhere on .
Enumerate the nonempty countable collection of selected indices as , allowing repetitions if needed, and choose positive weights summing to one. The mixture has density by [F4], so exactly on up to a -null set. If , then . For each , ; these sets cover up to the null set where is not positive on . Hence . Also every vanishes on by step 3.1, so . Since was arbitrary, for all .
Depends on
- Dominated statistical experiments
- Likelihood densities
- The Axiom of Choice
- Finite, sigma-finite, and semifinite measures
- A sigma-finite signed measure that is absolutely continuous with respect to a sigma-finite positive measure has a unique almost-everywhere density
- Monotone convergence for the integral
Used by
Dependency tree · two levels
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Sources
- Ryan Martin, Lecture Notes on Advanced Statistical Theory (standard reference, not scraped)
- Joshua M. Tebbs, STAT 512 Mathematical Statistics Lecture Notes (standard reference, not scraped)
- Peter Kempthorne, MIT 18.655 Lecture Note 6: Sufficiency (standard reference, not scraped)
- Noah Fithian, Stat 210a Reader: Sufficiency (standard reference, not scraped)
- Noah Fithian, Stat 210a Reader: Completeness, Ancillarity, and Basu’s Theorem (standard reference, not scraped)
- Charles J. Geyer, Stat 5421 Lecture Notes: Exponential Families (standard reference, not scraped)
- Jiahao Tian, Chapter 2: Sufficiency and Exponential Families (standard reference, not scraped)