Using geometry to select one dimensional exponential families that are monotone likelihood ratio in the sample space, are weakly unimodal and can be parametrized by a measure of central tendency
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Publication:296490
DOI10.3390/E16074088zbMath1338.60043OpenAlexW2069887321MaRDI QIDQ296490
Publication date: 15 June 2016
Published in: Entropy (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3390/e16074088
Parametric inference (62F99) Probability distributions: general theory (60E05) Geometric probability and stochastic geometry (60D99)
Cites Work
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- Decomposition of Kullback-Leibler risk and unbiasedness for parameter-free estimators
- Maximum likelihood estimators uniformly minimize distribution variance among distribution unbiased estimators in exponential families
- An alternative representation of Altham's multiplicative-binomial distribution
- Double Exponential Families and Their Use in Generalized Linear Regression
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