activity
20042021
most citedNon-Markovian diffusion equations and processes: analysis and simulations

81 citations · 495 across the 57 of their papers we have counts for

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Showing 2010Show all

6 papers · 1 filter

math.PR2010★ 1 cited

Berry-Esseen and Edgeworth approximations for the tail of an infinite sum of weighted gamma random variables

Mark S. Veillette, Murad S. Taqqu

Consider the sum , where are i.i.d.~gamma random variables with shape parameter , and the 's are predetermined we…

q-fin.ST2010

Maximum penalized quasi-likelihood estimation of the diffusion function

Jeff Hamrick, Yifei Huang, Constantinos Kardaras +1

We develop a maximum penalized quasi-likelihood estimator for estimating in a nonparametric way the diffusion function of a diffusion process, as an alternative to more traditional…

math.PR2010★ 1 cited

Large scale behavior of wavelet coefficients of non-linear subordinated processes with long memory

Marianne Clausel, François Roueff, Murad S. Taqqu +1

We study the asymptotic behavior of wavelet coefficients of random processes with long memory. These processes may be stationary or not and are obtained as the output of non--linea…

cs.DS2010★ 2 cited

Max-stable sketches: estimation of Lp-norms, dominance norms and point queries for non-negative signals

Stilian A. Stoev, Murad S. Taqqu

Max-stable random sketches can be computed efficiently on fast streaming positive data sets by using only sequential access to the data. They can be used to answer point and Lp-nor…

math.PR2010

Technique for computing the PDFs and CDFs of non-negative infinitely divisible random variables

Mark S. Veillette, Murad S. Taqqu

We present a method for computing the PDF and CDF of a non-negative infinitely divisible random variable . Our method uses the Lévy-Khintchine representation of the Laplace tran…

math.PR2010★ 2 cited

Distribution functions of Poisson random integrals: Analysis and computation

Mark S. Veillette, Murad S. Taqqu

We want to compute the cumulative distribution function of a one-dimensional Poisson stochastic integral $I(\krnl) = \displaystyle \int_0^T \krnl(s) N(ds)$, where is a Poisson…