Sample quantiles of additive renewal reward processes

1996 ◽  
Vol 33 (04) ◽  
pp. 1018-1032 ◽  
Author(s):  
Angelos Dassios
Keyword(s):  
Brownian Motion ◽  
Random Processes ◽  
Independent Increments ◽  
Look Back ◽  
Renewal Reward Processes ◽  
Reward Process ◽  
Renewal Reward Process ◽  
Sample Quantiles

The distribution of the sample quantiles of random processes is important for the pricing of some of the so-called financial ‘look-back' options. In this paper a representation of the distribution of the α-quantile of an additive renewal reward process is obtained as the sum of the supremum and the infimum of two rescaled independent copies of the process. This representation has already been proved for processes with stationary and independent increments. As an example, the distribution of the α-quantile of a randomly observed Brownian motion is obtained.

Sample quantiles of additive renewal reward processes

1996 ◽  
Vol 33 (4) ◽  
pp. 1018-1032 ◽  
Author(s):  
Angelos Dassios
Keyword(s):  
Brownian Motion ◽  
Random Processes ◽  
Independent Increments ◽  
Look Back ◽  
Renewal Reward Processes ◽  
Reward Process ◽  
Renewal Reward Process ◽  
Sample Quantiles

The distribution of the sample quantiles of random processes is important for the pricing of some of the so-called financial ‘look-back' options. In this paper a representation of the distribution of the α-quantile of an additive renewal reward process is obtained as the sum of the supremum and the infimum of two rescaled independent copies of the process. This representation has already been proved for processes with stationary and independent increments. As an example, the distribution of the α-quantile of a randomly observed Brownian motion is obtained.

Policy Improvement and the Newton–Raphson Algorithm for Renewal Reward Processes

1989 ◽  
Vol 3 (3) ◽  
pp. 393-396 ◽  
Author(s):  
J. M. McNamara
Keyword(s):  
Continuous Time ◽  
Successive Approximations ◽  
Average Reward ◽  
Policy Improvement ◽  
Unique Root ◽  
Renewal Reward Processes ◽  
Reward Process ◽  
Newton Raphson ◽  
Renewal Reward Process ◽  
Raphson Algorithm

We consider a renewal reward process in continuous time. The supremum average reward, γ* for this process can be characterised as the unique root of a certain function. We show how one can apply the Newton–Raphson algorithm to obtain successive approximations to γ*, and show that the successive approximations so obtained are the same as those obtained by using the policy improvement technique.

scholarly journals The density of a passage time for a renewal-reward process perturbed by a diffusion

2013 ◽  
Vol 26 (1) ◽  
pp. 108-112 ◽  
Author(s):  
Christophette Blanchet-Scalliet ◽  
Diana Dorobantu ◽  
Didier Rullière
Keyword(s):  
Passage Time ◽  
Reward Process ◽  
Renewal Reward Process

BROWNIAN MOTION MINUS THE INDEPENDENT INCREMENTS: REPRESENTATION AND QUEUING APPLICATION

2020 ◽  
pp. 1-25
Author(s):  
Kerry Fendick
Keyword(s):  
Brownian Motion ◽  
Maximum Likelihood ◽  
Markov Process ◽  
Maximum Likelihood Estimators ◽  
Single Server ◽  
Single Server Queue ◽  
Stationary Increments ◽  
Transient Distribution ◽  
Independent Increments ◽  
Server Queue

This paper relaxes assumptions defining multivariate Brownian motion (BM) to construct processes with dependent increments as tractable models for problems in engineering and management science. We show that any Gaussian Markov process starting at zero and possessing stationary increments and a symmetric smooth kernel has a parametric kernel of a particular form, and we derive the unique unbiased, jointly sufficient, maximum-likelihood estimators of those parameters. As an application, we model a single-server queue driven by such a process and derive its transient distribution conditional on its history.

scholarly journals Estimation of the integral of a stochastic process

1978 ◽  
Vol 18 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Noel Cressie
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
Stochastic Processes ◽  
Poisson Process ◽  
Covariance Structure ◽  
Regression Equation ◽  
Linear Drift ◽  
Independent Increments ◽  
Sampling Points ◽  
Optimum Sampling

Consider the class of stochastic processes with stationary independent increments and finite variances; notable members are brownian motion, and the Poisson process. Now for Xt any member of this class of processes, we wish to find the optimum sampling points of Xt, for predicting . This design question is shown to be directly related to finding sampling points of Yt for estimating β in the regression equation, Yt = β + Xt. Since processes with stationary independent increments have linear drift, the regression equation for Yt is the first type of departure we might look for; namely quadratic drift, and unchanged covariance structure.

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