Research Papers

Interval Predictor Models With a Linear Parameter Dependency

[+] Author and Article Information
Luis G. Crespo

Dynamic Systems and Control Branch,
NASA Langley Research Center,
Hampton, VA 23681
e-mail: Luis.G.Crespo@nasa.gov

Sean P. Kenny, Daniel P. Giesy

Dynamic Systems and Control Branch,
NASA Langley Research Center,
Hampton, VA 23681

Manuscript received April 21, 2015; final manuscript received October 15, 2015; published online January 6, 2016. Assoc. Editor: Sumanta Acharya.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Verif. Valid. Uncert 1(2), 021007 (Jan 06, 2016) (10 pages) Paper No: VVUQ-15-1019; doi: 10.1115/1.4032070 History: Received April 21, 2015; Revised October 15, 2015

This paper develops techniques for constructing metamodels that predict the range of an output variable given input–output data. We focus on models depending linearly on the parameters and arbitrarily on the input. This structure enables to rigorously characterize the range of the predicted output and the uncertainty in the model’s parameters. Strategies for calculating optimal interval predictor models (IPMs) that are insensitive to outliers are proposed. The models are optimal in the sense that they yield an interval valued function of minimal spread containing all (or, depending on the formulation, most) of the observations. Outliers are identified as the IPM is calculated by evaluating the extent by which their inclusion into the dataset degrades the tightness of the prediction. When the data generating mechanism (DGM) is stationary, the data are independent, and the optimization program (OP) used for calculating the IPM is convex (or when its solution coincides with the solution to an auxiliary convex program); the model’s reliability, which is the probability that a future observation would fall within the predicted range, is bounded tightly using scenario optimization theory. In contrast to most alternative techniques, this framework does not require making any assumptions on the underlying structure of the DGM.

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Grahic Jump Location
Fig. 1

IPM A: type-1 IPM for all N = 150 observations

Grahic Jump Location
Fig. 2

IPM B: type-1 IPM after the removal of five outliers

Grahic Jump Location
Fig. 3

IPM C: type-2 IPM for λ = 145/150

Grahic Jump Location
Fig. 4

Empirical CDF Fρ(p¯̂, p¯̂) for IPM A, IPM B, and IPM C

Grahic Jump Location
Fig. 5

Predicted intervals resulting from alternative metamodeling techniques




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