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Independent product inputs have an orthogonal Hoeffding decomposition
Statement
Let be finite, let have a finite product probability law, and let . With the components of Product-law Hoeffding ANOVA components, each depends only on , every nonempty component has mean zero, and for every its coordinate average satisfies . Distinct components are orthogonal, and
Facts & Assumptions
Given: A finite product probability law, a real , and the components defined above.
Product-coordinate averaging operators: averages over coordinates outside and lifts the result to the product space.
Product-coordinate projections commute and contract in L2: , is self-adjoint, and .
Moments, variance, and covariance on a probability space: variance is the squared norm after subtracting the mean.
Proof
Given: The objects in the Statement and facts [F1]–[F4].
Interchange the two finite sums in . The coefficient of is , which is zero unless , when it is one. Since , this proves reconstruction.
Fix nonempty and . Apply to the finite sum defining . For each , exactly the terms and both become by [F2]; their signs are opposite. All terms cancel, so . Also , since every satisfies . Thus depends only on , and for nonempty its mean is zero by applying [F2] to the preceding identity.
Let . Choose in their symmetric difference; after interchanging their names if needed, take , and put . Since , the projection composition rule [F2] and step 2.1 give . Since and , the same rule gives . Self-adjointness of therefore gives .
By step 1.1, . Step 3.1 makes this a finite orthogonal sum, so its squared norm is the sum of the squared component norms. Apply [F4] to identify that norm with .
Depends on
- Product-law Hoeffding ANOVA components
- Product-coordinate projections commute and contract in L2
- Fubini's theorem for L^1 functions on a sigma-finite product
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
- Cauchy-Schwarz inequality for $L^2$
- Moments, variance, and covariance on a probability space
- Product-coordinate averaging operators
Used by
- First-order and total Sobol indices Definition
- A constant output has no normalized Sobol indices Example
- Polynomial-chaos coefficients group by variable support Example
- The Ishigami function's only possible interaction is X1-X3 Example
- Complete orthonormal tensor expansions group into Sobol components Theorem
- Coordinate Poincare inequalities bound total Sobol effects Theorem
- Pick-freeze covariances recover first and total Sobol numerators Theorem
Dependency tree · two levels
21 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
- Thierry Klein, Introduction to Sensitivity Analysis: The Statistical Study of Sobol Indices, complete lecture notes, Theorem 2.1 and Corollary 2.1 (standard reference, not scraped)
- Art B. Owen, Variance Components and Generalized Sobol' Indices, §§2–2.1 (standard reference, not scraped)