Least Squares Parameter Estimation for Sparse Functional Varying Coefficient Model

In the present paper, we study functional varying coefficient model in which both the response and the predictor are functions. We give estimates of the intercept and the slope functions in the case that the observations are sparse and noise-contaminated longitudinal data by using least squares repr...

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Hlavní autoři: Behdad Mostafaiy, Mohammad Reza Faridrohani
Médium: Artigo
Jazyk:Inglês
Vydáno: Springer 2017-08-01
Edice:Journal of Statistical Theory and Applications (JSTA)
Témata:
Functional varying coefficient model; Longitudinal data Analysis; Rate of convergence; Regularization; Reproducing kernel Hilbert space; Sparsity.
On-line přístup:https://www.atlantis-press.com/article/25883866.pdf
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