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An external dominating law need not be a valid sufficiency reference
Statement refuted
The implication “ sufficient and for every imply is -measurable” is false if the reference probability is not required to belong to the family (or to share its conditional law given ).
Facts & Assumptions
Given: with its full -field; ; , , and zero mass on the other two points; ; and uniform on four points.
Sufficient statistics: sufficiency is one common conditional law of the data given .
Standard Borel spaces: finite sets with their full sigma-fields are standard Borel, since the discrete metric is complete and separable.
Likelihood densities: a density relative to is the pointwise RN derivative on this finite space.
Probability measures and probability spaces: a probability law assigns total mass one and nonnegative masses to the four points.
The Dirac set function at a point: the Dirac set function is .
Measure kernel and probability kernel: a probability kernel has measurable event evaluations and probability-measure sections.
Proof
Define for . Each section is the Dirac set function [F5]: it vanishes on the empty set, has total mass one, and is countably additive because a disjoint family contains in at most one member. Thus each section is a probability measure [F4]. All evaluations are measurable on the finite full sigma-field, so [F6] makes a probability kernel independent of , between the standard Borel spaces in [F2]. For every and , . Hence is sufficient by [F1].
Every atom of has mass , so . The density values are if and zero if . For fixed , the two points and have the same value but different derivative values. Every atom has positive -mass, so no modification on an -null set can change them; the derivative has no -measurable version.
This refutes the implication with an arbitrary external reference. Taking the reference to be a member restores the common conditional law needed for the criterion: the conditional kernel at the reference is then the model’s shared kernel. The counterexample’s has conditional second coordinate uniform given , while each model law has second coordinate zero, identifying the precise missing hypothesis.
Depends on
Used by
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Dependency tree · two levels
23 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
- 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)
- Jiahao Tian, Chapter 2: Sufficiency and Exponential Families (standard reference, not scraped)