Practical guidance for testing the accuracy of deconvolution results from quantal analysis.
A Monte Carlo study was carried out to test the reliability of the Maximum Likelihood Estimator (MLE) approach for quantal analysis. This widely used statistical method was applied to extract a finite mixture of Gaussian distributions from simulated data. The data were generated by convolving a distribution of discrete amplitude steps (multiples of a unitary step Q) with Gaussian noise of various standard deviations (sigma n). Our results offer practical guidance on when to use the MLE, taking into account the determining parameters: signal to noise ratio (Q/sigma n, the most important parameter), number of samples collected and the number of components (k). For a given set of parameters the algorithm always converged to the "true" values, never converged to the "true" values or converged in only a fraction of cases to the "true" values. The behavior of the fitting routine in the parameter space is displayed in contour plots. These contour plots can be used as a guide to test the accuracy of deconvolution results.
Lüscher
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Institute of Physiology, University of Berne, Switzerland.
Clamann
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Lüscher
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D003198:Computer Simulation; D008959:Models, Neurological; D009010:Monte Carlo Method