title: Graph Estimation with Joint Additive Models.
abstract: In recent years, there has been considerable interest in estimating conditional independence graphs in high dimensions. Most previous work has assumed that the variables are multivariate Gaussian, or that the conditional means of the variables are linear; in fact, these two assumptions are nearly equivalent. Unfortunately, if these assumptions are violated, the resulting conditional independence estimates can be inaccurate. We propose a semi-parametric method, graph estimation with joint additive models, which allows the conditional means of the features to take on an arbitrary additive form. We present an efficient algorithm for our estimator's computation, and prove that it is consistent. We extend our method to estimation of directed graphs with known causal ordering. Using simulated data, we show that our method performs better than existing methods when there are non-linear relationships among the features, and is comparable to methods that assume multivariate normality when the conditional means are linear. We illustrate our method on a cell-signaling data set.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, 98195-7232, U.S.A.
authors.forename: Arend
authors.initials: A
authors.lastname: Voorman
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, 98195-7232, U.S.A.
authors.forename: Ali
authors.initials: A
authors.lastname: Shojaie
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, 98195-7232, U.S.A.
authors.forename: Daniela
authors.initials: D
authors.lastname: Witten
keywords: Conditional independence; Graphical model; Lasso; Non-Gaussianity; Nonlinearity; Sparse additive model; Sparsity