IngentaConnect Characterizing the Variability of System Kernel and Input Estimat...

Author: Verotta D.¹

Source: Annals of Biomedical Engineering, Volume 26, Number 5, September 1998 , pp. 870-882(13)

Publisher: Springer

Abstract:

The identification of the input to, or kernels of, a system using nonparametric representations and least-squares estimation is becoming increasingly popular. Nonparametric representations avoid making a priori assumptions about the input or having detailed knowledge about the system, and only need to guarantee known general characteristics (for example, positivity), which are obtained through the imposition of constraints on the estimates. An often overlooked problem is how to characterize the variability of the estimates so obtained. This problem is caused by the presence of constraints—and/or the nonlinearities of the estimates, or the complexity of the (regression based) estimation algorithms used—which make standard methods of estimating variability incorrect. In this article we investigate the use of a resampling technique called the “bootstrap” to obtain the desired estimates of variability. We present real data analysis demonstrating the approach, and through simulations we test the performance of a novel bootstrap technique obtaining confidence bands for the estimated functions. © 1998 Biomedical Engineering Society. PAC98: 8710+e, 0270Lq

Keywords: Bootstrap; Inequality constraints; Splines; System kernel estimation: variability

Document Type: Research article

DOI: 10.1114/1.110

Affiliations: 1: Department of Biopharmaceutical Sciences and Pharmaceutical Chemistry, University of California, San Francisco, CA and Department of Epidemiology and Biostatistics, University of California, San Francisco, CA

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Characterizing the Variability of System Kernel and Input Estimates