Oblique plans for a binomial process

2016 ◽  
Vol 4 (6) ◽  
Author(s):  
R. Magiera ◽  
S. Trybuła
Keyword(s):  
Binomial Process

scholarly journals Fractional negative binomial and Pólya processes

2018 ◽  
Vol 38 (1) ◽  
pp. 77-101
Author(s):  
Palaniappan Vellai Samy ◽  
Aditya Maheshwari
Keyword(s):  
Poisson Process ◽  
Negative Binomial ◽  
Random Variable ◽  
Infinitely Divisible ◽  
One Dimensional ◽  
Fractional Poisson Process ◽  
Negative Binomial Process ◽  
The One ◽  
Definition Of ◽  
Binomial Process

In this paper, we define a fractional negative binomial process FNBP by replacing the Poisson process by a fractional Poisson process FPP in the gamma subordinated form of the negative binomial process. It is shown that the one-dimensional distributions of the FPP and the FNBP are not infinitely divisible. Also, the space fractional Pólya process SFPP is defined by replacing the rate parameter λ by a gamma random variable in the definition of the space fractional Poisson process. The properties of the FNBP and the SFPP and the connections to PDEs governing the density of the FNBP and the SFPP are also investigated.

Using EWMA control schemes for monitoring wafer quality in negative binomial process

2011 ◽  
Vol 51 (2) ◽  
pp. 400-405 ◽  
Author(s):  
Fong-Jung Yu ◽  
Yung-Yu Yang ◽  
Ming-Jaan Wang ◽  
Zhang Wu
Keyword(s):  
Negative Binomial ◽  
Control Schemes ◽  
Negative Binomial Process ◽  
Binomial Process

Negative binomial processes

1969 ◽  
Vol 6 (03) ◽  
pp. 633-647 ◽  
Author(s):  
Ole Barndorff-Nielsen ◽  
G. F. Yeo
Keyword(s):  
Finite Number ◽  
Compact Subset ◽  
Gaussian Processes ◽  
Negative Binomial ◽  
Sum Of Squares ◽  
Poisson Processes ◽  
Intensity Parameter ◽  
Negative Binomial Process ◽  
Binomial Process

Summary This paper is concerned with negative binomial processes which are essentially mixed Poisson processes whose intensity parameter is given by the sum of squares of a finite number of independently and identically distributed Gaussian processes. A study is made of the distribution of the number of points of a k-dimensional negative binomial process in a compact subset of Rk , and in particular in the case where the underlying Gaussian processes are independent Ornstein-Uhlenbeck processes when more detailed results may be obtained.

scholarly journals On a Fractional Binomial Process

2011 ◽  
Vol 146 (3) ◽  
pp. 646-662 ◽  
Author(s):  
Dexter O. Cahoy ◽  
Federico Polito
Keyword(s):  
Binomial Process

scholarly journals L’exploration des ressources extractives non renouvelables : théorie économique, processus stochastique et vérification

2009 ◽  
Vol 53 (4) ◽  
pp. 559-586 ◽  
Author(s):  
Ph. Crabbé
Keyword(s):  
Economic Theory ◽  
Negative Binomial ◽  
Empirical Support ◽  
Turn Of The Century ◽  
Geological Information ◽  
Théorie Économique ◽  
Negative Binomial Process ◽  
The One ◽  
Binomial Process ◽  
Made In

AbstractMost of the literature devoted to the "theory of the mine" has been developed under certainty. It has been unable to explain the activity of exploration. The stochastic models of exploration were developed quasi-independently from economic theory. The purpose of this article is to survey both the mining and economic literature related to the "theory of the mine" under uncertainty and the exploration models since the turn of the century. The survey is complementary to the one made in this journal by F. Peterson and A.C. Fisher.The first part defines exploration as being essentially a search and information gathering activity. It reviews the contributions to the economic theory of exploration and resource stock uncertainty. It compares the optimal extraction path and the life cycle of the mine under stock uncertainty and stock certainty. It shows in particular that increasing the rate of discount is generally inappropriate to take account of stock uncertainty. Some partial equilibrium results on exploration are given as well. The presence of stock uncertainty or exploration in a general equilibrium model is shown to jeopardize the optimality of competitive allocations.The second part points out the wealth of the theoretical and empirical analysis of exploration as a stochastic process. It first reviews the literature on size distributions of reserves which gives strong theoretical and empirical support to the lognormal hypothesis. It then goes on to the exploration models which roughly speaking can be broken down into two groups. The Allais type models, better suited for relatively unexplored regions, which combine a Poisson or negative binomial process for discovery with a lognormal distribution for sizes. The Arps-Roberts-Kaufman type models, more adequate for "mature" regions, assume exhaustive sampling with probability proportional to size of discovery. Generally the treatment of the discovery process, to be distinguished from the sampling for sizes, and the handling of geological information are still woefully inadequate.The third and last part of the survey points out the gap which exists in the microeconomic literature about the study of random inputs. It suggests that the theory of dams and insurance and the theory of search especially adaptive search could be fruitfully used. Problems which remain to be tackled are the influence of stock uncertainty on grade of ore mined and on investment in capacity.

scholarly journals Mathematical Fun with the Compound Binomial Process

1988 ◽  
Vol 18 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Hans U. Gerber
Keyword(s):  
Joint Distribution ◽  
Classical Result ◽  
Poisson Model ◽  
Risk Theory ◽  
Binomial Model ◽  
Probability Of Ruin ◽  
Starting Point ◽  
Compound Binomial ◽  
Compound Binomial Model ◽  
Binomial Process

AbstractThe compound binomial model is a discrete time analogue (or approximation) of the compound Poisson model of classical risk theory. In this paper, several results are derived for the probability of ruin as well as for the joint distribution of the surpluses immediately before and at ruin. The starting point of the probabilistic arguments are two series of random variables with a surprisingly simple expectation (Theorem 1) and a more classical result of the theory of random walks (Theorem 2) that is best proved by a martingale argument.

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