1,732,437 research outputs found
Suzanne Resnick
No full textSuzanne Resnick, born February 14, 1933, in Paris, France, to immigrants, provides Holocaust survivor testimony. Interviewers: Zelda Kaplan and Ann Solov Walker. Note Taker: Betty Ann Fishman. Produced by Holocaust Center, Boston North, Peabody, Massachusetts. Taped at Comcast Studios, Peabody, Mass
Douglas Resnick, Flute Recital, March 8, 1986
No full textConcert program for Douglas Resnick, Flute Recital, March 8,
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Danielle Resnick, "LAUNCH EVENT: 2019 Global Food Policy Report"
No full textDanielle Resnick SPECIAL EVENT LAUNCH EVENT: 2019 Global Food Policy Report Washington, DC, USA MAR 27, 2019 - 12:15 PM TO 01:45 PM ED
The influence of dependence on data network models
No full textConsider an infinite-source marked Poisson process to model end user inputs to a data network. At Poisson times, connections are initated. The connection is characterized by a triple (F, L, R) denoting the total quantity of transmitted data in a connection, the length or duration of the connection, and the transmission rate; the three quantities are related by F = LR. How critical is the dependence structure of the mark for network characteristics such as burstiness, distribution tails of cumulative input, and long-range dependence properties of traffic measured in consecutive time slots? In a previous publication (D'Auria and Resnick (2006)) we assumed that F and R were independent. Here we assume that L and R are independent. The change in dependence assumptions means that the model properties change dramatically: tails of cumulative input per time slot are dramatically heavier, traffic cannot be approximated by a Gaussian distribution, and the decay of dependence cannot be measured in the traditional way using correlation functions. Different network applications are likely to have different mark dependence structure. We argue that the present independence assumption on L and R is likely to be appropriate for network applications such as streaming media or peer-to-peer networks. Our conclusion is that it is desirable to separate network traffic by application and to model each application with its own appropriate dependence structure. © Applied Probability Trust 2008
Simulation of Brown-Resnick Processes
No full textBrown–Resnick processes form a flexible class of stationary max-stable
processes based on Gaussian random fields. With regard to applications, fast and
accurate simulation of these processes is an important issue. In fact, Brown–Resnick
processes that are generated by a dissipative flow do not allow for good finite approximations
using the definition of the processes. On large intervals we get either huge
approximation errors or very long operating times. Looking for solutions of this problem,
we give different representations of the Brown–Resnick processes—including
random shifting and a mixed moving maxima representation—and derive various
kinds of finite approximations that can be used for simulation purposes. Furthermore,
error bounds are calculated in the case of the original process by Brown and
Resnick (J Appl Probab 14(4):732–739, 1977). For a one-parametric class of Brown–
Resnick processes based on the fractional Brownian motion we perform a simulation
study and compare the results of the different methods concerning their approximation
quality. The presented simulation techniques turn out to provide remarkable
improvements
Brown-Resnick Processes: Analysis, Inference and Generalizations
No full textThis thesis deals with the analysis, inference and further generalizations of
a rich and flexible class of max-stable random fields, the so-called
Brown-Resnick processes. The first chapter gives the explicit distribution
of the shape functions in the mixed moving maxima representation of the
original Brown-Resnick process based on Brownian motions. The result is particularly
useful for a fast simulation method. In chapter 2, a multivariate peaks-over-threshold
approach for parameter estimation of Hüsler-Reiss
distributions, a popular model in multivariate extreme value theory, is presented.
As Hüsler-Reiss distributions constitute the finite dimensional margins of
Brown-Resnick processes based on Gaussian random fields, the estimators directly
enable statistical inference for this class of max-stable processes. As an application,
a non-isotropic Brown-Resnick process is fitted to the extremes of 12-year
data of daily wind speed measurements.
Chapter 3 is concerned with the definition of Brown-Resnick processes
based on Lévy processes on the whole real line. Amongst others, it is
shown that these Lévy-Brown-Resnick processes naturally arise as
limits of maxima of stationary stable Ornstein-Uhlenbeck processes.
The last chapter is devoted to the study of maxima of d-variate Gaussian triangular
arrays, where in each row the random vectors are assumed to be independent, but
not necessarily identically distributed. The row-wise maxima converge
to a new class of multivariate max-stable distributions, which can be seen as
max-mixtures of Hüsler-Reiss distributions
The Social Design Reader di Elizabeth Resnick
No full textRecensione al volume: Elizabeth Resnick (a cura di), The Social Design Reader, Bloomsbury Publishing, 2019, pp. 476
63: Science journalism (with Brian Resnick)
No full textDan and James chat about science journalism with Brian Resnick, who is a science reporter at Vox.co
Alice Resnick Runs For Appeals Court
No full textAlice Resnick of Toledo (Ohio) Municipal Court is running for a seat on the Sixth District Court of Appeals
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