 Semi-parametric order-based generalized multivariate regressionMilad Kharratzadeh and Mark Coates Journal of Multivariate Analysis, 2017, vol. 156, issue C, 89-102 Abstract: In this paper, we consider a generalized multivariate regression problem where the responses are some functions of linear transformations of predictors. We assume that these functions are strictly monotonic, but their form and parameters are unknown. We propose a semi-parametric estimator based on the ordering of the responses which is invariant to the functional form of the transformation function as long as it is strictly monotonic. We prove that our estimator, which maximizes the rank similarity between responses and linear transformations of predictors, is a consistent estimator of the true coefficient matrix. We also identify the rate of convergence and show that the squared estimation error decays with a rate of o(1/n). We then propose a greedy algorithm to maximize the highly non-smooth objective function of our model and examine its performance through extensive simulations. Finally, we compare our algorithm with traditional multivariate regression algorithms over synthetic and real data. Keywords: Coefficient of agreement; Generalized multivariate regression; Kendall-type measure; Rank correlation; Semi-parametric regression (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:156:y:2017:i:c:p:89-102 Ordering information: This journal article can be ordered from DOI: 10.1016/j.jmva.2017.01.012 Access Statistics for this article Journal of Multivariate Analysis is currently edited by de Leeuw, J. More articles in Journal of Multivariate Analysis from Elsevier |
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