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Extended t-process regression models

Lookup NU author(s): Dr Jian Shi

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Abstract

© 2017 Elsevier B.V. Gaussian process regression (GPR) model has been widely used to fit data when the regression function is unknown and its nice properties have been well established. In this article, we introduce an extended t-process regression (eTPR) model, a nonlinear model which allows a robust best linear unbiased predictor (BLUP). Owing to its succinct construction, it inherits many attractive properties from the GPR model, such as having closed forms of marginal and predictive distributions to give an explicit form for robust procedures, and easy to cope with large dimensional covariates with an efficient implementation. Properties of the robustness are studied. Simulation studies and real data applications show that the eTPR model gives a robust fit in the presence of outliers in both input and output spaces and has a good performance in prediction, compared with other existed methods.


Publication metadata

Author(s): Wang Z, Shi JQ, Lee Y

Publication type: Article

Publication status: Published

Journal: Journal of Statistical Planning and Inference

Year: 2017

Volume: 189

Pages: 38-60

Print publication date: 01/10/2017

Online publication date: 24/05/2017

Acceptance date: 11/05/2017

ISSN (print): 0378-3758

ISSN (electronic): 1873-1171

Publisher: Elsevier BV

URL: https://doi.org/10.1016/j.jspi.2017.05.006

DOI: 10.1016/j.jspi.2017.05.006


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