We study MCMC algorithms for Bayesian analysis of a linear regression model with generalized hyperbolic errors. The Markov operators associated with the standard data augmentation algorithm and a sandwich variant of that algorithm are shown to be trace-class.

Maximum likelihood estimation of the concentration parameter of von Mises–Fisher distributions involves inverting the ratio <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$R_\nu=I_{\nu +1} / I_\nu $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <msub> <mi>R</mi> <mi mathvariant="italic">ν</mi> </msub> <mo>=</mo> <msub> <mi>I</mi> <mrow> <mi mathvariant="italic">ν</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo stretchy="false">/</mo> <msub> <mi>I</mi> <mi mathvariant="italic">ν</mi> </msub> </mrow> </math> </EquationSource> </InlineEquation> of modified Bessel functions and computational methods are required to invert these functions using...</equationsource></equationsource></inlineequation>

We reveal the boundary bias problem of Birnbaum–Saunders, inverse Gaussian, and reciprocal inverse Gaussian kernel estimators (Jin and Kawczak, 2003; Scaillet, 2004) and re-formulate these estimators to solve the problem. We investigate asymptotic properties of a new class of asymmetric kernel...

Multivariate Laplace distribution is an important stochastic model that accounts for asymmetry and heavier than Gaussian tails, while still ensuring the existence of the second moments. A Lévy process based on this multivariate infinitely divisible distribution is known as Laplace motion, and...

A characterization is provided for the von Mises–Fisher random variable, in terms of first exit point from the unit hypersphere of the drifted Wiener process. Laplace transform formulae for the first exit time from the unit hypersphere of the drifted Wiener process are provided. Post...