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Diebolt, J., Ip, E. and Olkin, I. (1994) A Stochastic EM Algorithm for Approximating the Maximum Likelihood Estimate. Technical Report 301, Department of Statistics, Stanford University, Stanford.

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• 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.