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...

Täydet tiedot

Tallennettuna:
Bibliografiset tiedot
Päätekijät: Behdad Mostafaiy, Mohammad Reza Faridrohani
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: Springer 2017-08-01
Sarja:Journal of Statistical Theory and Applications (JSTA)
Aiheet:
Functional varying coefficient model; Longitudinal data Analysis; Rate of convergence; Regularization; Reproducing kernel Hilbert space; Sparsity.
Linkit:https://www.atlantis-press.com/article/25883866.pdf
Tagit: Lisää tagi
Ei tageja, Lisää ensimmäinen tagi!

Samankaltaisia teoksia