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@article{991158, author = {Koštál, Lubomír and Pokora, Ondřej}, article_number = {7}, doi = {http://dx.doi.org/10.3390/e14071221}, keywords = {statistical dispersion; entropy; Fisher information; nonparametric density estimation}, language = {eng}, issn = {1099-4300}, journal = {Entropy}, title = {Nonparametric estimation of information-based measures of statistical dispersion}, volume = {14}, year = {2012} }
TY - JOUR ID - 991158 AU - Koštál, Lubomír - Pokora, Ondřej PY - 2012 TI - Nonparametric estimation of information-based measures of statistical dispersion JF - Entropy VL - 14 IS - 7 SP - 1221–1233 EP - 1221–1233 SN - 10994300 KW - statistical dispersion KW - entropy KW - Fisher information KW - nonparametric density estimation N2 - 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. ER -
KOŠTÁL, Lubomír and Ondřej POKORA. Nonparametric estimation of information-based measures of statistical dispersion. \textit{Entropy}. 2012, vol.~14, No~7, p.~1221–1233. ISSN~1099-4300. Available from: https://dx.doi.org/10.3390/e14071221.
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