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Reproduction of secondary data in projection pursuit transformation

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Abstract

A challenge when working with multivariate data in a geostatistical context is that the data are rarely Gaussian. Multivariate distributions may include nonlinear features, clustering, long tails, functional boundaries, spikes, and heteroskedasticity. Multivariate transformations account for such features so that they are reproduced in geostatistical models. Projection pursuit as developed for high dimensional data exploration can also be used to transform a multivariate distribution into a multivariate Gaussian distribution with an identity covariance matrix. Its application within a geostatistical modeling context is called the projection pursuit multivariate transform (PPMT). An approach to incorporate exhaustive secondary variables in the PPMT is introduced. With this approach the PPMT can incorporate any number of secondary variables with any number of primary variables. A necessary alteration to the approach to make this numerically practical was the implementation of a continuous probability estimator that relies on Bernstein polynomials for the transformation that takes place in the projections. Stop** criteria were updated to incorporate a bootstrap t test that compares data sampled from a multivariate Gaussian distribution with the data undergoing transformation.

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Authors and Affiliations

  1. Department of Civil & Environmental Engineering, Faculty of Engineering, School of Mining & Petroleum Engineering, 7-203 Donadeo Innovation Centre for Engineering, 9211 - 116 Street NW, Edmonton, AB, T6G 1H9, Canada

    John G. Manchuk, Ryan M. Barnett & Clayton V. Deutsch

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  1. John G. Manchuk
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  2. Ryan M. Barnett
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  3. Clayton V. Deutsch
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Correspondence to John G. Manchuk.

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Manchuk, J.G., Barnett, R.M. & Deutsch, C.V. Reproduction of secondary data in projection pursuit transformation. Stoch Environ Res Risk Assess 31, 2585–2605 (2017). https://doi.org/10.1007/s00477-016-1363-y

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