Parthanil Roy

Parameter estimation for exponentially tempered power law distributions

Tail estimates are developed for power law probability distributions with exponential tempering, using a conditional maximum likelihood approach based on the upper-order statistics. Tempered power law distributions are intermediate between heavy power-law tails and Laplace or exponential tails, and are sometimes called “semi-heavy” tailed distributions. The estimation method is demonstrated on simulated data from a tempered stable distribution, and for several data sets from geophysics and finance that show a power law probability tail with some tempering.

Ergodic properties of sum- and max-stable stationary random fields via null and positive group actions

We establish characterization results for the ergodicity of stationary symmetric

ERGODIC THEORY, ABELIAN GROUPS, AND POINT PROCESSES INDUCED BY STABLE RANDOM FIELDS

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Nonsingular group actions and stationary S alpha S random fields

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Point process convergence for branching random walks with regularly varying steps

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Maxima of stable random fields, nonsingular actions and finitely generated abelian groups: A survey

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A large sample test for the length of memory of stationary symmetric stable random fields via nonsingular ℤd-actions

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Branching random walk with step size coming from a power law

-Frechet random fields. We show that the result of Samorodnitsky [Ann. Probab. 33 (2005) 1782-1803] remains valid in the multiparameter setting, that is, a stationary S

Stationary Symmetric α-Stable Discrete Parameter Random Fields

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Multivariate extremes of generalized skew-normal distributions

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