Functional Analysis of Variance for Hilbert-Valued Multivariate Fixed Effect Models release_xpyyd6opovhutd4zscffh6hcom [as of editgroup_7ymtjomtu5gjvl3vrtl47mvpte]

by M.D. Ruiz-Medina

Released as a article .

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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Type  article
Stage   submitted
Date   2015-05-17
Version   v1
Language   en ?
arXiv  1505.04379v1
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