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A redundant Bernoulli parameterization has a nonidentifiable coordinate
Statement refuted
For every parametric statistical model with parameter , the first-coordinate target is identifiable.
Facts & Assumptions
Given: The parameter set , finite sample space, law family and target stated above.
Sigma-algebras: a sigma-algebra contains the whole space and is closed under complements and countable unions; the full power set satisfies these conditions.
Measurable spaces and measurable sets: a measurable space is a set together with a sigma-algebra on it.
Probability measures and probability spaces: a probability measure has total mass and is countably additive on measurable sets.
Statistical experiments: an experiment is a parameter-indexed family of probability measures on the sample measurable space.
Parametric statistical models: each parameter is sent to its sampling law .
Estimands on a statistical model: the first coordinate is a function to .
Identifiability of an estimand: an estimand is identifiable only if equal sampling laws imply equal target values.
Identifiability is constancy on observational classes: identifiability is equivalent to a unique factorization of the target through the set of model laws.
Counterexample
Take with and
For each , define
and set . The law depends only on the sum, while the target is the first coordinate.
Given: The redundant two-parameter Bernoulli family and first-coordinate target stated above.
The full power set contains and is closed under finite unions and complements, so it is an algebra of subsets. Every countable union of its members is still a subset of , so it is a sigma-algebra by [F1] and gives a measurable space by [F2]. For every , both masses and are nonnegative and sum to . In a pairwise disjoint sequence of events, each singleton and appears at most once, so the displayed formula is countably additive. Thus is a probability measure by [F3], and the family is an experiment by [F4].
The points and have coordinates in , and their sums are and , so both belong to . Both measures assign to and to ; the four events consequently have probabilities under both laws, so . But . By [F7], is not identifiable; by [F8], it cannot factor through the model-law set.
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