Souvik Ghosh
When does the mean excess plot look linear?.
Wed, 12/01/2010 - 16:01 — SomNambulAuthors: Sidney I. Resnick, Souvik Ghosh
Subjects: Statistics
AbstractIn risk analysis, the mean excess plot is a commonly used exploratory
plotting technique for confirming iid data is consistent with a generalized
Pareto assumption for the underlying distribution, since in the presence of
such a distribution thresholded data have a mean excess plot that is roughly
linear. Does any other class of distributions share this linearity of the plot?
Under some extra assumptions, we are able to conclude that only the generalized
Pareto family has this property.A functional large and moderate deviation principle for infinitely divisible processes driven by null-recurrent markov chains.
Fri, 10/22/2010 - 15:01 — SomNambulAuthors: Souvik Ghosh
Subjects: Probability
AbstractSuppose $ E$ is a space with a null-recurrent Markov kernel $ P$.
Furthermore, suppose there are infinite particles with variable weights on $ E$
performing a random walk following $ P$. Let $ X_{t}$ be a weighted functional
of the position of particles at time $ t$.A strong law for the rate of growth of long latency periods in cloud computing service.
Tue, 10/19/2010 - 15:01 — SomNambulAuthors: Souvik Ghosh, Soumyadip Ghosh
Subjects: and Cluster Computing, Distributed, Parallel
AbstractCloud-computing shares a common pool of resources across customers at a scale
that is orders of magnitude larger than traditional multi-user systems.
Constituent physical compute servers are allocated multiple "virtual machines"
(VM) to serve simultaneously. Each VM user should ideally be unaffected by
others' demand. Naturally, this environment produces new challenges for the
service providers in meeting customer expectations while extracting an
efficient utilization from server resources. We study a new cloud service
metric that measures prolonged latency or delay suffered by customers.Weak limits for exploratory plots in the analysis of extremes.
Tue, 08/17/2010 - 15:02 — SomNambulAuthors: Souvik Ghosh, Bikramjit Das
Subjects: Statistics
AbstractExploratory plotting tools have been devised aplenty in order to diagnose the
goodness-of-fit of data sets to a hypothesized distribution. Some of them have
found extensive use in diverse areas of finance, telecommunication,
environmental science, etc. in order to detect sub-exponential or heavy-tailed
behavior in observed data. In this paper we concentrate on two such plotting
methodologies: the Quantile-Quantile plots for heavy-tails and the Mean Excess
plots.A Discussion on Mean Excess Plots.
Thu, 06/03/2010 - 15:01 — SomNambulAuthors: Souvik Ghosh, Sidney I Resnick
Subjects: Probability
AbstractA widely used tool in the study of risk, insurance and extreme values is the
mean excess plot. One use is for validating a generalized Pareto model for the
excess distribution. This paper investigates some theoretical and practical
aspects of the use of the mean excess plot.Long Strange Segments, Ruin Probabilities and the Effect of Memory on Moving Average Processes.
Tue, 02/16/2010 - 16:01 — SomNambulAuthors: Gennady Samorodnitsky, Souvik Ghosh
Subjects: Probability
AbstractWe obtain the rate of growth of long strange segments and the rate of decay
of infinite horizon ruin probabilities for a class of infinite moving average
processes with exponentially light tails. The rates are computed explicitly. We
show that the rates are very similar to those of an i.i.d. process as long as
moving average coefficients decay fast enough. If they do not, then the rates
are significantly different. This demonstrates the change in the length of
memory in a moving average process associated with certain changes in the rate
of decay of the coefficients.
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