Functional Analysis of Variance for Hilbert-Valued Multivariate Fixed
Effect Models
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[as of editgroup_7ymtjomtu5gjvl3vrtl47mvpte]
by
M.D. Ruiz-Medina
2015
Abstract
This paper presents new results on Functional Analysis of Variance for fixed
effect models with correlated Hilbert-valued Gaussian error components. The
geometry of the Reproducing Kernel Hilbert Space (RKHS) of the error term is
considered in the computation of the total sum of squares, the residual sum of
squares, and the sum of squares due to the regression. Under suitable linear
transformation of the correlated functional data, the distributional
characteristics of these statistics, their moment generating and characteristic
functions, are derived. Fixed effect linear hypothesis testing is finally
formulated in the Hilbert-valued multivariate Gaussian context considered.
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1505.04379v1
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