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  • Open Access

Maximum penalized likelihood estimation of interspike interval distribution

BMC Neuroscience201314 (Suppl 1) :P219

https://doi.org/10.1186/1471-2202-14-S1-P219

  • Published:

Keywords

  • Probability Density Function
  • Likelihood Estimation
  • Neuronal Activity
  • Quantitative Method
  • Alternative Measure

We address the problem of non-parametric estimation of the recently proposed measures of statistical dispersion of positive continuous random variables. The measures are based on the concepts of differential entropy and Fisher information and describe the "spread" or "variability" of the random variable from a different point of view than the ubiquitously used concept of standard deviation. The maximum penalized likelihood (MPL) estimation [1, 2] of the probability density function is applied and a methodology how to estimate the dispersion measures is presented. We illustrate the approach on three standard statistical models describing the neuronal activity.

Frequently, the dispersion of measured data needs to be described. Although standard deviation is used ubiquitously for quantification of variability, such approach has limitations. For example highly variable data might not be random at all if it consists only of "extremely small" and "extremely large" measurements. Although the probability density function, or its estimate provide a complete view, quantitative methods are needed in order to compare different models or experimental results. In a series of recent studies, some alternative measures of dispersion were proposed. However, the estimation of these coefficients from data is more problematic. We present the obtained estimations of the dispersion coefficients based on the MPL method, which extends our previous study [3].

Authors’ Affiliations

(1)
Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
(2)
Institute of Physiology, Academy of Sciences of the Czech Republic, Prague, Czech Republic

References

  1. Good IJ, Gaskins RA: Nonparametric roughness penalties for probability densities. Biometrika. 1971, 58: 255-277.View ArticleGoogle Scholar
  2. Eggermont PPB, LaRiccia VN: Maximum Penalized Likelihood Estimation: Volume I. Springer. 2001Google Scholar
  3. Kostal L, Pokora O: Nonparametric estimation of information-based measures of statistical dispersion. Entropy. 2012, 14: 1221-1233. 10.3390/e14071221.View ArticleGoogle Scholar

Copyright

© Pokora and Kostal; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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