Abstract
This paper presents the application of two correction methods for co-variate measurement error to nonparametric regression. We focus on a recent and due to its sparsity properties very promising smoothing approach coming from the area of machine learning, the Relevance Vector Machine (RVM), developed by Tipping (2000). Two correction methods for measurement error are then applied to the RVM: regression calibration (Carroll et al. (1995)) and the SIMEX method (Carroll et al. (1995)). We show why standard regression calibration fails and present a simulation study that indicates an improvement of the RVM regression in terms of bias when SIMEX correction is applied.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Rummel, D. (2005). The Relevance Vector Machine Under Covariate Measurement Error. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_33
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DOI: https://doi.org/10.1007/3-540-28084-7_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25677-9
Online ISBN: 978-3-540-28084-2
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