Estimation of sample mean and variance for Monte-Carlo simulations

Value at Risk (VaR) and Tail Value at Risk (TVaR) are two measures that are commonly used to quantify the risk associated with a loss severity distribution. In this paper, both values are calculated analytically and estimated using a Monte Carlo simulation when the loss severity random variable has an alpha power Pareto distribution. This distribution is the result of alpha power transformation on a Pareto distribution. The random numbers used in the Monte Carlo simulation are generated from the alpha power Pareto distribution using the inverse transformation technique. In the special case used, the estimated VaR and TVaR values obtained from the Monte Carlo simulation for some security levels used are close to the actual VaR and TVaR values as long as the number of random numbers generated in the Monte Carlo simulation is sufficiently large.

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“…n dengan rumus yang bergantung pada definisi dari fungsi g. [17], nilai dugaan bagi γ dapat dihitung dengan rumus berikut:…”

Section: Simulasi Monte Carlounclassified

Value at Risk dan Tail Value at Risk dari Peubah Acak Besarnya Kerugian yang Menyebar Alpha Power Pareto

2023

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“…The mean delay of packets is calculated using the sample mean calculation in [12]. Let the variable n be the number of packets transmitted from ONUs to OLT.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The packet delay variance is calculated using the sample variance calculation in [12]. The packet delay variance s 2 is as follows.…”

Section: Performance Evaluationmentioning

confidence: 99%

Dynamic Band Allocation Method of XGS-PON System Using Time Sliding Window Traffic Prediction

Han¹

2021

APJCRI

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The dynamic bandwidth allocation (DBA) algorithm has the greatest impact on the system performance in a 10-Gbps-capable symmetric passive optical network (XGS-PON). Most DBA algorithms perform DBA using only transmission requests sent from optical network units (ONUs) to optical line termination (OLT). In this paper, a new method to improve the performance by predicting the packet arrival rate of ONU and by using the predicted value and the transmission request of ONU at the same time in DBA is introduced. After the first dynamic bandwidth allocation with the request, the second dynamic bandwidth allocation is performed with the remaining bandwidth and predicted packet length. A new prediction method is proposed by applying the time sliding window method to the packet arrival rate prediction. In the proposed method, instead of predicting the packet arrival rate whenever one packet arrives, the packet arrival rate is predicted in unit time. The computer simulation showed that the proposed method performs better in packet mean delay than a method without packet prediction and a method using aggressive packet prediction. In addition, the length of the sliding window that optimizes the packet arrival rate prediction performance under burst traffic conditions is determined through simulation.

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“…are respectively the approximate mean and standard deviation of Z. If ∆ is a comparison operator (=, =, ≥, ≤, <, >) and z is a given value of Z, then in order to estimate the value of the mean μ, the standard deviation σ and the probability P(Z ∆ z), we can use these formulas (Thomas and Luk, 2008)…”

Section: Using Monte Carlo Analysis In Abssmentioning

confidence: 99%

Stability-based model for evacuation system using agent-based social simulation and Monte Carlo method

Naili

Bourahla

Naili

2019

IJSPM

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The agent-based modelling is used for modelling many complex dynamic systems, especially those including autonomous individuals such as human beings' societies, animals' societies, robots, insects' societies, etc. Evacuation systems such as those needed for supermarket buildings are considered as complex dynamic systems. In these systems, we have to deal with the problem of rescuing a high number of people of different ages, sex, physical characteristics, etc. Furthermore, this process mostly runs in buildings with different constraints like locations of the rows of shelves, exit gates, etc. On one hand, in order to deal with disasters such as fire propagation, studying this kind of system using a dynamic model has a great importance in order to avoid the maximum of casualties. On the other hand, the model that represents this kind of system must take into account several factors such as time, the building's characteristics and people's characteristics. In this study, an agent-based model has been designed to visualise the dynamic system behaviour via these internal entities that often interact. Additionally, we use some dynamic data mining methods such as Monte Carlo method to calculate the stable characteristics of this model via probabilistic approach.

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Estimation of sample mean and variance for Monte-Carlo simulations

Abstract: Abstract

Cited by 11 publications

References 9 publications

Value at Risk dan Tail Value at Risk dari Peubah Acak Besarnya Kerugian yang Menyebar Alpha Power Pareto

Value at Risk dan Tail Value at Risk dari Peubah Acak Besarnya Kerugian yang Menyebar Alpha Power Pareto

Dynamic Band Allocation Method of XGS-PON System Using Time Sliding Window Traffic Prediction

Stability-based model for evacuation system using agent-based social simulation and Monte Carlo method

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