A generalized normal distribution

By Saralees Nadarajah. 2005

540 citations recorded by the source corpus.

Subfields: Finance, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/da6b21a4301391b78a41142461a906e5a09264df

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Citations in the corpus

Data reconciliation and gross error detection for operational data in power plants (2014, background): “These distributions have very different shapes and can be expressed in a more general form of a generalized normal distribution with different shape parameters [28].”
A General and Adaptive Robust Loss Function (2017, background): “Our distribution resembles the generalized Gaussian distribution [28, 33], except that it is “smoothed” so as to approach a Gaussian distribution near the origin regardless of the shape parameter α .”
On the formulation of the alternative hypothesis for geodetic outlier detection (2013, background): “Inherent alternatives: according to mechanism (a), all observation errors come from the same non-normal distribution: HA : yi ∼ W (θ) , θ ∈ A, i = 1, . . . , n (10) Candidates for W are leptokurtic distributions like generalized normal distribution (Nadarajah 2005) or Student’s t distribution.”
Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England (2019, methodology): “The generalised normal distribution, introduced by Nadarajah (2005), is similar to the Student’s t distribution in that it has a kurtosis parameter and nests the normal distribution.”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, methodology): “[2003]), namely the generalized Gaussian distribution [Nadarajah, 2005].”
Log-gamma-generated families of distributions (2014, background): “The generalizations are made, for instance, for gamma [2], inverse Gaussian [3], Pareto [4], beta [5], normal [6], power-normal [7] and bivariate power-normal [8] distributions.”