On characterizations of distributions by mean absolute deviation and variance bounds

DOI10.1007/BF00118636zbMath0782.62018OpenAlexW2162636431MaRDI QIDQ1207630

Ramesh M. Korwar

Publication date: 1 April 1993

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00118636

zbMATH Keywords

characterizationsquadratic functionlinearitymean absolute deviationvariance boundsdouble Lomax distributiongeneralized hypergeometric distributionsmixture of two Waring distributionsPearson system of distributions

Mathematics Subject Classification ID

Characterization and structure theory of statistical distributions (62E10)

Related Items (12)

The generalized double Lomax distribution with applications ⋮ A characterization of the Pearson system of distributions and the associated orthogonal polynomials ⋮ Chernoff-type inequality and variance bounds ⋮ On infinite covariance expansions ⋮ First-order covariance inequalities via Stein's method ⋮ Unified extension of variance bounds for integrated Pearson family ⋮ An extended Stein-type covariance identity for the Pearson family with applications to lower variance bounds ⋮ Further results based on Chernoff-type inequalities ⋮ General characterization theorems based on versions of the Chernoff inequality and the Cox representation ⋮ Some results on lower variance bounds useful in reliability modeling and estimation ⋮ Moment-Based Inference for Pearson's QuadraticqSubfamily of Distributions ⋮ General characterization theorems via the mean absolute deviation

Cites Work

• Characterization of probability distributions by Poincaré-type inequalities
• The central limit theorem and Poincaré-type inequalities
• Characterizations of distributions by variance bounds
• A note on an inequality involving the normal distribution
• An inequality for the multivariate normal distribution
• On assessing multivariate normality based on Shapiro-Wilk W statistic
• On an Inequality and a Related Characterization of the Normal Distribution
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