TITLE:
Sparse Additive Gaussian Process with Soft Interactions
AUTHORS:
Garret Vo, Debdeep Pati
KEYWORDS:
Additive, Gaussian Process, Interaction, Lasso, Sparsity, Variable Selection
JOURNAL NAME:
Open Journal of Statistics,
Vol.7 No.4,
July
31,
2017
ABSTRACT: This paper presents a novel variable selection
method in additive nonparametric regression model. This work is motivated by
the need to select the number of nonparametric components and number of
variables within each nonparametric component. The proposed method uses a
combination of hard and soft shrinkages to separately control the number of
additive components and the variables within each component. An efficient
algorithm is developed to select the importance of variables and estimate the
interaction network. Excellent performance is obtained in simulated and real
data examples.