Title
On decompositions of multivariate functions
Abstract
We present formulas that allow us to decompose a function f of d variables into a sum of 2(d) terms f(u) indexed by subsets u of {1,..,d} where each term f(u) depends only on the variables with indices in u. The decomposition depends on the choice of d commuting projections {P-j}(j=1)(d) where P-j(f) does not depend on the variable x(j). We present an explicit formula for f(u), which is new even for the ANOVA and anchored decompositions; both are special cases of the general decomposition. We show that the decomposition is minimal in the following sense: if f is expressible as a sum in which there is no term that depends on all of the variables indexed by the subset z, then, for every choice of {P-j}(j=1)(d), the terms f(u) = 0 for all subsets u containing z. Furthermore, in a reproducing kernel Hilbert space setting, we give sufficient conditions for the terms f(u) to be mutually orthogonal.
Year
DOI
Venue
2010
10.1090/S0025-5718-09-02319-9
MATHEMATICS OF COMPUTATION
Keywords
Field
DocType
indexation,reproducing kernel hilbert space
Hilbert space,Multivariate statistics,Mathematical analysis,Decomposition method (constraint satisfaction),Numerical analysis,Reproducing kernel Hilbert space,Mathematics
Journal
Volume
Issue
ISSN
79
270
0025-5718
Citations 
PageRank 
References 
18
1.33
11
Authors
4
Name
Order
Citations
PageRank
Frances Y. Kuo147945.19
Ian H. Sloan21180183.02
Grzegorz W. Wasilkowski3527167.51
Henryk Wozniakowski413028.31