Informace o publikaci

Nonparametric estimation of information-based measures of statistical dispersion

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KOŠTÁL Lubomír POKORA Ondřej

Rok publikování 2012
Druh Článek v odborném periodiku
Časopis / Zdroj Entropy
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
Doi http://dx.doi.org/10.3390/e14071221
Obor Obecná matematika
Klíčová slova statistical dispersion; entropy; Fisher information; nonparametric density estimation
Popis 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 estimation of the probability density function proposed by Good and Gaskins is applied and a complete methodology of how to estimate the dispersion measures with a single algorithm is presented. We illustrate the approach on three standard statistical models describing neuronal activity.

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