Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jmva.2008.02.018
Title: On Stein's lemma, dependent covariates and functional monotonicity in multi-dimensional modeling
Authors: Zhang, C.
Li, J. 
Meng, J.
Keywords: 62E15
62F30
62H10
62H20
Additive model
Nonparametric regression
Partially monotone function
primary
secondary
Similarly ordered
Stein's Lemma
Support vector machine
Issue Date: Nov-2008
Citation: Zhang, C., Li, J., Meng, J. (2008-11). On Stein's lemma, dependent covariates and functional monotonicity in multi-dimensional modeling. Journal of Multivariate Analysis 99 (10) : 2285-2303. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jmva.2008.02.018
Abstract: Tracking the correct directions of monotonicity in multi-dimensional modeling plays an important role in interpreting functional associations. In the presence of multiple predictors, we provide empirical evidence that the observed monotone directions via parametric, nonparametric or semiparametric fit of commonly used multi-dimensional models may entirely violate the actual directions of monotonicity. This breakdown is caused primarily by the dependence structure of covariates, with negligible influence from the bias of function estimation. To examine the linkage between the dependent covariates and monotone directions, we first generalize Stein's Lemma for random variables which are mutually independent Gaussian to two important cases: dependent Gaussian, and independent non-Gaussian. We show that in both two cases, there is an explicit one-to-one correspondence between the monotone directions of a multi-dimensional function and the signs of a deterministic surrogate vector. Moreover, we demonstrate that the second case can be extended to accommodate a class of dependent covariates. This generalization further enables us to develop a de-correlation transform for arbitrarily dependent covariates. The transformed covariates preserve modeling interpretability with little loss in modeling efficiency. The simplicity and effectiveness of the proposed method are illustrated via simulation studies and real data application. © 2008 Elsevier Inc. All rights reserved.
Source Title: Journal of Multivariate Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/105266
ISSN: 0047259X
DOI: 10.1016/j.jmva.2008.02.018
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