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Nonparametric Estimation of Information-Based Measures of Statistical Dispersion
- 1.0378876 - FGÚ 2013 RIV CH eng J - Journal Article
Košťál, Lubomír - Pokora, Ondřej
Nonparametric Estimation of Information-Based Measures of Statistical Dispersion.
Entropy. Roč. 14, č. 7 (2012), s. 1221-1233. E-ISSN 1099-4300
R&D Projects: GA ČR(CZ) GAP103/11/0282; GA ČR(CZ) GBP304/12/G069; GA ČR(CZ) GPP103/12/P558
Institutional support: RVO:67985823
Keywords : statistical dispersion * entropy * Fisher information * nonparametric density estimation * neuronal activity
Subject RIV: FH - Neurology
Impact factor: 1.347, year: 2012
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 of positive random variables with a single algorithm is presented. The approach is illustrated on three standard statistical models describing neuronal activity
Permanent Link: http://hdl.handle.net/11104/0210209
Number of the records: 1