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Likelihood ratio as a Radon–Nikodym derivative
Definition
Let and be probability measures on the same measurable space. Assume AC. Their Lebesgue decomposition relative to is the unique decomposition with and (Every sigma-finite signed measure admits a Lebesgue decomposition relative to a sigma-finite positive measure, The Lebesgue decomposition of a sigma-finite signed measure is unique). Both components are positive by Lebesgue components of a positive finite measure are positive, and is finite because their sum has mass one. Define the likelihood-ratio density of against to be the -almost-everywhere class
of The Radon-Nikodym derivative as an almost-everywhere equivalence class. Under AC this class has a nonnegative measurable representative by A sigma-finite positive measure has a nonnegative Radon–Nikodym density version.
This is a full density of with respect to only in the absolutely continuous case . If that condition fails, the singular component remains separate. A displayed representative is not a canonical pointwise function: its values are determined only -almost everywhere.
Depends on
- Probability measures and probability spaces
- Finite, sigma-finite, and semifinite measures
- The Axiom of Choice
- The total variation |nu|(E) from countable measurable partitions
- Absolute continuity of a signed or complex measure with respect to a positive measure
- Mutual singularity for signed or complex measures
- Every sigma-finite signed measure admits a Lebesgue decomposition relative to a sigma-finite positive measure
- The Lebesgue decomposition of a sigma-finite signed measure is unique
- Lebesgue components of a positive finite measure are positive
- The Radon-Nikodym derivative as an almost-everywhere equivalence class
- A sigma-finite positive measure has a nonnegative Radon–Nikodym density version
Used by
- Sequential likelihood-ratio test Algorithm
- A singular pair has no full likelihood ratio Counterexample
- Kullback–Leibler divergence Definition
- Bernoulli likelihood ratio and change of measure Example
- Finite-sample likelihood ratio for iid observations Lemma
- Conditional-intensity inter-event law and point-pattern likelihood Theorem
- Iid drift conditions imply almost-sure test termination Theorem
- Likelihood-ratio change of measure Theorem
- Terminal error bounds for a sequential likelihood-ratio test Theorem
Dependency tree · two levels
38 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, §§2.2 and 3.2, PDF pp. 30–33 and 54–55 (standard reference, not scraped)
- Richard F. Bass, Real Analysis for Graduate Students, §13.3 (book pagination) (standard reference, not scraped)
- John K. Hunter, Measure Theory, Theorem 6.27 and proof, §6.8 (notes pp. 75–77) (standard reference, not scraped)