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Sample Paths in . . .
, 2005
"... We consider a class of convergence questions for infinite products that arise in wavelet theory when the wavelet filters are more singular than is traditionally built into the assumptions. We establish pointwise convergence properties for the absolute square of the scaling functions. Our proofs are ..."
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We consider a class of convergence questions for infinite products that arise in wavelet theory when the wavelet filters are more singular than is traditionally built into the assumptions. We establish pointwise convergence properties for the absolute square of the scaling functions. Our proofs are based on probabilistic tools.
SamplePath Large Deviations
"... Samplepath large deviations for tandem and priority queues with Gaussian inputs ..."
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Samplepath large deviations for tandem and priority queues with Gaussian inputs
SamplePath Optimization in Simulation
 In Proceedings of the Winter Simulation Conference
, 1994
"... This paper summarizes information about a method, called samplepath optimization, for optimizing performance functions in certain stochastic systems that can be modeled by simulation. We explain the method, give conditions under which it converges, and display some sample calculations that indicate ..."
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This paper summarizes information about a method, called samplepath optimization, for optimizing performance functions in certain stochastic systems that can be modeled by simulation. We explain the method, give conditions under which it converges, and display some sample calculations
Variablenumber samplepath optimization
"... The samplepath method is one of the most important tools in simulationbased optimization. The basic idea of the method is to approximate the expected simulation output by the average of sample observations with a common random number sequence. In this paper, we describe a new variant of Powell’s ..."
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Cited by 13 (1 self)
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The samplepath method is one of the most important tools in simulationbased optimization. The basic idea of the method is to approximate the expected simulation output by the average of sample observations with a common random number sequence. In this paper, we describe a new variant of Powell’s
Sample path properties and . . .
, 2001
"... We consider an infinite extension K of a local field of zero characteristic which is a union of an increasing sequence of finite extensions. K is equipped with an inductive limit topology; its conjugate K is a completion of K with respect to a topology given by certain explicitly written seminorms. ..."
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We consider an infinite extension K of a local field of zero characteristic which is a union of an increasing sequence of finite extensions. K is equipped with an inductive limit topology; its conjugate K is a completion of K with respect to a topology given by certain explicitly written seminorms. The semigroup of measures, which defines a stablelike process X(t) on K, is concentrated on a compact subgroup S ⊂ K. We study properties of the process XS(t), a part of X(t) in S. It is shown that XS(t) is recurrent, the Hausdorff and packing dimensions of the image of an interval equal 0 almost surely. In the case of tamely ramified extensions a correct Hausdorff measure for this set is found.
Sample Path Methods In The Control Of Queues
 Queueing Systems
, 1995
"... Sample path methods are now among the most used techniques in the control of queueing systems. However, due to the lack of mathematical formalism, they may appear to be nonrigorous and even sometimes mysterious. The goal of this paper is threefold: to provide a general mathematical setting, to surve ..."
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Cited by 12 (0 self)
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Sample path methods are now among the most used techniques in the control of queueing systems. However, due to the lack of mathematical formalism, they may appear to be nonrigorous and even sometimes mysterious. The goal of this paper is threefold: to provide a general mathematical setting
Online Learning with Sample Path Constraints
"... We study online learning when the objective of the decision maker is to maximize her longterm average reward subject to certain sample path average constraints. We define the rewardinhindsight as the highest reward the decision maker could have achieved, while satisfying the constraints, had she ..."
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Cited by 16 (9 self)
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We study online learning when the objective of the decision maker is to maximize her longterm average reward subject to certain sample path average constraints. We define the rewardinhindsight as the highest reward the decision maker could have achieved, while satisfying the constraints, had she
Finite state Markovchain approximations to univariate and vector autoregressions
 Economics Letters
, 1986
"... The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1. ..."
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Cited by 493 (0 self)
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The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1.
Option pricing when underlying stock returns are discontinuous
 Journal of Financial Economics
, 1976
"... The validity of the classic BlackScholes option pricing formula dcpcnds on the capability of investors to follow a dynamic portfolio strategy in the stock that replicates the payoff structure to the option. The critical assumption required for such a strategy to be feasible, is that the underlying ..."
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Cited by 1001 (3 self)
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stock return dynamics can be described by a stochastic process with a continuous sample path. In this paper, an option pricing formula is derived for the moregeneral cast when the underlying stock returns are gcncrated by a mixture of both continuous and jump processes. The derived formula has most
STOCHASTIC PROCESSES WITH SAMPLE PATH FUNCTIONS OF BOUNDED VARIATION by
, 1968
"... A simple condition is given for an arbitrary random function to have its sample path functions be of bounded variation. This condition is necessary when the random function has independent, integrable increments. The sample path Stieltjes stochastic integral is defined and a few properties are given ..."
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A simple condition is given for an arbitrary random function to have its sample path functions be of bounded variation. This condition is necessary when the random function has independent, integrable increments. The sample path Stieltjes stochastic integral is defined and a few properties
Results 1  10
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