A Family of Symmetric Distributions on the Circle

By M. Chris Jones, Arthur Pewsey. 2005

125 citations recorded by the source corpus.

Subfields: Applied Mathematics, Artificial Intelligence, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/2a7b03423225e13e1afb4a47950dd6c5af74173d

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In Applied Mathematics

In Artificial Intelligence

In Environmental Engineering

Citations in the corpus

Phase Reconstruction from Amplitude Spectrograms Based on Von-Mises-Distribution Deep Neural Network (2018, unknown): “Discussion The generalized version of the von Mises distribution is the generalized cardioid distribution [17] given as P (GC) (yt,f ;μ, κ, ψ) = (cosh (κψ)) (1 + tanh (κψ) cos (yt,f − μ)) 2πP1/ψ (cosh (κψ)) , (8)”
On Families of Distributions with Shape Parameters (2015, unknown): “One, ﬁrst used in diﬀerent form by Batschelet (1981), is the parameter − 1 ≤ ν ≤ 1 is a skewness parameter, and the transformation might be used with three-parameter symmetric g (e.g. the symmetric family of Jones & Pewsey, 2005).”
A Family of Distributions on the Circle With Links to, and Applications Arising From, Möbius Transformation (2010, methodology): “Note that this symmetric subfamily has a direct competitor that also covers the von Mises and wrapped Cauchy distributions, namely the distributions of Jones and Pewsey (2005); we make a comparison in Section 3.5.”
Sine-skewed circular distributions (2011, methodology): “Results for the trigonometric moments of the Jones–Pewsey family are given in Jones and Pewsey (2005) .”
A tractable, parsimonious and flexible model for cylindrical data, with applications (2015, background): “It is to be noted that the circular marginal distribution for the GGSSVM is the sine-skewed Jones–Pewsey distribution (see [2, 10]).”
A new family of slash-distributions with elliptical contours (2007, background): “tionality constant is given in Table 1). For q > 0, ( 7 ) then becomes”
A CIRCULAR-CIRCULAR REGRESSION MODEL (2008, methodology): “If β1 is known, the maximum likelihood estimates of θ0 and φ are obtained by the recursive algorithm by Kent and Tyler (1988). The method of moments gives the estimators of θ0 and φ as follows: θ̂0 = arg(Cn + iSn) and φ̂ = 1 n |Cn + iSn|, where Cn = ∑n j=1 cos[θyj − θxj + 2arg{1 + re xj1}] and Sn = ”