The Experts below are selected from a list of 71964 Experts worldwide ranked by ideXlab platform
Sem Borst - One of the best experts on this subject based on the ideXlab platform.
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the supremum of a gaussian process over a random interval
Statistics & Probability Letters, 2004Co-Authors: Krzysztof Dȩbicki, Bert Zwart, Sem BorstAbstract:The aim of this note is to give the exact asymptotics ofwhere {X(t): t[greater-or-equal, slanted]0} is a centered Gaussian process with stationary increments and T is an independent non-negative random variable with regularly varying Tail Distribution. In addition, we obtain explicit lower and upper bounds for the prefactor. As an example we analyze the case of X(t) being a fractional Brownian motion and a Gaussian integrated process.
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the supremum of a gaussian process over a random interval
Statistics & Probability Letters, 2004Co-Authors: Krzysztof Dȩbicki, Bert Zwart, Sem BorstAbstract:Abstract The aim of this note is to give the exact asymptotics of P sup s∈[0,T] X(s)>u as u→∞, where {X(t) : t⩾0} is a centered Gaussian process with stationary increments and T is an independent non-negative random variable with regularly varying Tail Distribution. In addition, we obtain explicit lower and upper bounds for the prefactor. As an example we analyze the case of X(t) being a fractional Brownian motion and a Gaussian integrated process.
Tao Jiang - One of the best experts on this subject based on the ideXlab platform.
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large deviations for the stochastic present value of aggregate net claims with infinite variance in the renewal risk model and its application in risk management
Cluster Computing, 2018Co-Authors: Min Xiao, Ruixing Ming, Sheng Cui, Tao JiangAbstract:In insurance, if the insurer continuously invests her wealth in risk-free and risky assets, then the price process of the investment portfolio can be described as a geometric present Levy process. People always are interested in estimating the Tail Distribution of the stochastic present value of aggregate claims. In this paper, the large deviation for the stochastic present value of aggregate net claims, when the net claim size Distribution is of Pareto type with finite expectation are obtained. We conduct some simulations to check the accuracy of the result we obtained and consider a portfolio optimization problem that maximizes the expected terminal wealth of the insurer subject to a solvency constraint.
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large deviations for the stochastic present value of aggregate claims in the renewal risk model
Statistics & Probability Letters, 2015Co-Authors: Tao Jiang, Sheng Cui, Ruixing MingAbstract:In insurance, if the insurer continuously invests her wealth in risk-free and risky assets, then the price process of the investment portfolio can be described as a geometric Levy process. People always are interested in estimating the Tail Distribution of the stochastic present value of aggregate claims. In this paper, the large deviations for the stochastic present value of aggregate claims, when the claim size Distribution is of Pareto type with finite variance, are obtained.
Shizuo Sawada - One of the best experts on this subject based on the ideXlab platform.
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on the retention time Distribution of dynamic random access memory dram
IEEE Transactions on Electron Devices, 1998Co-Authors: Takeshi Hamamoto, S Sugiura, Shizuo SawadaAbstract:The retention time Distribution of high-density dynamic random access memory (DRAM) has been investigated. The key issue for controlling the retention time Distribution has been clarified and its model has been proposed for the first time. Trench capacitor cell with 0.6-/spl mu/m ground rule was evaluated. It was found that the retention time Distribution consists of "Tail Distribution" and "main Distribution." "Tail Distribution," by which DRAM refresh characteristics are restricted, depends on the boron concentration of the memory cell region. As boron concentration of the memory cell region increases, "Tail Distribution" is enhanced. This enhancement is due to the increase of the junction leakage current from the storage node. For the purpose of accounting for the nature of "Tail Distribution," the concept of thermionic field emission (TFE) current has been introduced. The high electric field at pn junction of the storage node enhances thermionic field emission from a deep level. The activation energy of the deep level is normally distributed among the memory cells, which leads to the normal Distribution of log(retention time). Two methods for reducing "Tail Distribution" are proposed. One is to reduce the electric field of the depletion layer of the storage node. The other is to reduce the concentration of the deep level for TFE current.
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Well concentration: a novel scaling limitation factor derived from DRAM retention time and its modeling
Proceedings of International Electron Devices Meeting, 2024Co-Authors: Takeshi Hamamoto, S Sugiura, Shizuo SawadaAbstract:A novel scaling limitation factor derived from DRAM retention time and its modeling has been proposed. So far, the well concentration has been optimized from the viewpoint of the scaling of the transistor dimensions. However, it has been found that the DRAM retention time strongly depends on the well concentration. Increase of the well concentration enhances thermionic field emission (TFE) current from the storage node. This leakage current makes "Tail Distribution" of the retention time. Therefore, the well concentration must be optimized taking into account the retention time Distribution.
Ruixing Ming - One of the best experts on this subject based on the ideXlab platform.
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large deviations for the stochastic present value of aggregate net claims with infinite variance in the renewal risk model and its application in risk management
Cluster Computing, 2018Co-Authors: Min Xiao, Ruixing Ming, Sheng Cui, Tao JiangAbstract:In insurance, if the insurer continuously invests her wealth in risk-free and risky assets, then the price process of the investment portfolio can be described as a geometric present Levy process. People always are interested in estimating the Tail Distribution of the stochastic present value of aggregate claims. In this paper, the large deviation for the stochastic present value of aggregate net claims, when the net claim size Distribution is of Pareto type with finite expectation are obtained. We conduct some simulations to check the accuracy of the result we obtained and consider a portfolio optimization problem that maximizes the expected terminal wealth of the insurer subject to a solvency constraint.
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large deviations for the stochastic present value of aggregate claims in the renewal risk model
Statistics & Probability Letters, 2015Co-Authors: Tao Jiang, Sheng Cui, Ruixing MingAbstract:In insurance, if the insurer continuously invests her wealth in risk-free and risky assets, then the price process of the investment portfolio can be described as a geometric Levy process. People always are interested in estimating the Tail Distribution of the stochastic present value of aggregate claims. In this paper, the large deviations for the stochastic present value of aggregate claims, when the claim size Distribution is of Pareto type with finite variance, are obtained.
Bruno Sinopoli - One of the best experts on this subject based on the ideXlab platform.
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kalman filtering with intermittent observations Tail Distribution and critical value
IEEE Transactions on Automatic Control, 2012Co-Authors: Bruno SinopoliAbstract:In this paper, we analyze the performance of Kalman filtering for discrete-time linear Gaussian systems, where packets containing observations are dropped according to a Markov process modeling a Gilbert-Elliot channel. To address the challenges incurred by the loss of packets, we give a new definition of non-degeneracy, which is essentially stronger than the classical definition of observability, but much weaker than one-step observability, which is usually used in the study of Kalman filtering with intermittent observations. We show that the trace of the Kalman estimation error covariance under intermittent observations follows a power decay law. Moreover, we are able to compute the exact decay rate for non-degenerate systems. Finally, we derive the critical value for non-degenerate systems based on the decay rate, improving upon the state of the art.
