scholarly journals Control charts for skewed distributions

2012 ◽  
Vol 9 (2) ◽  
Author(s):  
Derya Karagöz ◽  
Canan Hamurkaroğlu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Control Charts ◽  
Type I ◽  
Skewed Distributions ◽  
Simulation Results ◽  
Standard Deviations ◽  
Control Limits

In this paper the control limits of \(\bar{X}\) and \(R\) control charts for skewed distributions are obtained by considering the classic, the weighted variance (\(\mathit{WV}\)), the weighted standard deviations (\(\mathit{WSD}\)) and the skewness correction (\(\mathit{SC}\)) methods. These methods are compared by using Monte Carlo simulation. Type I risk probabilities of these control charts are compared with respect to different subgroup sizes for skewed distributions which are Weibull, gamma and lognormal. Simulation results show that Type I risk of \(\mathit{SC}\) method is less than that of other methods. When the distribution is approximately symmetric, then the Type I risks of Shewhart, \(\mathit{WV}\) , \(\mathit{WSD}\), and \(\mathit{SC}\) \(\bar{X}\) charts are comparable, while the \(\mathit{SC}\) \(R\) chart has a noticeable smaller Type I risk.

Coping with uncertainties in integrated urban water management

1997 ◽  
Vol 36 (8-9) ◽  
pp. 265-269
Author(s):  
Govert D. Geldof
Keyword(s):  
Water Management ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Interdisciplinary Approach ◽  
Urban Water ◽  
Urban Water Management ◽  
Integrated Water Management ◽  
Good Design ◽  
Management Techniques ◽  
Simulation Results

In the practice of integrated water management we meet complexity, subjectivity and uncertainties. Uncertainties come into play when new urban water management techniques are applied. The art of a good design is not to reduce uncertainties as much as possible, but to find the middle course between cowardice and recklessness. This golden mean represents bravery. An interdisciplinary approach is needed to reach consensus. Calculating uncertainties by using Monte Carlo simulation results may be helpful.

scholarly journals Cheating at Craps

2021 ◽  
Vol 48 (4) ◽  
pp. 53-61
Author(s):  
Andrea Marin ◽  
Carey Williamson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Modeling And Simulation ◽  
Analytical Model ◽  
Quantitative Evaluation ◽  
Mathematical Analysis ◽  
Simulation Modeling ◽  
Analytical Modeling ◽  
The World ◽  
Simulation Results

Craps is a simple dice game that is popular in casinos around the world. While the rules for Craps, and its mathematical analysis, are reasonably straightforward, this paper instead focuses on the best ways to cheat at Craps, by using loaded (biased) dice. We use both analytical modeling and simulation modeling to study this intriguing dice game. Our modeling results show that biasing a die away from the value 1 or towards the value 5 lead to the best (and least detectable) cheating strategies, and that modest bias on two loaded dice can increase the winning probability above 50%. Our Monte Carlo simulation results provide validation for our analytical model, and also facilitate the quantitative evaluation of other scenarios, such as heterogeneous or correlated dice.

scholarly journals A Comparison of Different Data-driven Procedures to Determine the Bunching Window

2021 ◽  
Vol 49 (2) ◽  
pp. 262-293
Author(s):  
Vincent Dekker ◽  
Karsten Schweikert
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Income Distribution ◽  
Prior Information ◽  
Structural Breaks ◽  
Data Driven ◽  
Taxable Income ◽  
Counterfactual Models ◽  
Simulation Results ◽  
Second Procedure

In this article, we compare three data-driven procedures to determine the bunching window in a Monte Carlo simulation of taxable income. Following the standard approach in the empirical bunching literature, we fit a flexible polynomial model to a simulated income distribution, excluding data in a range around a prespecified kink. First, we propose to implement methods for the estimation of structural breaks to determine a bunching regime around the kink. A second procedure is based on Cook’s distances aiming to identify outlier observations. Finally, we apply the iterative counterfactual procedure proposed by Bosch, Dekker, and Strohmaier which evaluates polynomial counterfactual models for all possible bunching windows. While our simulation results show that all three procedures are fairly accurate, the iterative counterfactual procedure is the preferred method to detect the bunching window when no prior information about the true size of the bunching window is available.

scholarly journals The effect of social distancing on the reach of an epidemic in social networks

2021 ◽  
Author(s):  
Gregory Gutin ◽  
Tomohiro Hirano ◽  
Sung-Ha Hwang ◽  
Philip R. Neary ◽  
Alexis Akira Toda
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Social Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Disease Transmission ◽  
Global Level ◽  
Social Distancing ◽  
The Public ◽  
Simulation Results

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

Should a hotter paramagnet transform quicker to a ferromagnet? Monte Carlo simulation results for Ising model

2021 ◽  
Author(s):  
Subir K Das ◽  
Nalina Vadakkayil
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ising Model ◽  
Simulation Results ◽  
Heating Up

For quicker formation of ice, before inserting inside a refrigerator, heating up of a body of water can be beneficial. We report first observation of a counterpart of this intriguing...

scholarly journals Monte Carlo Modeling and Design of Photon Energy Attenuation Layers for >10× Quantum Yield Enhancement in Si-Based Hard X-ray Detectors

Instruments ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 17
Author(s):  
Eldred Lee ◽  
Kaitlin M. Anagnost ◽  
Zhehui Wang ◽  
Michael R. James ◽  
Eric R. Fossum ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Quantum Yield ◽  
Photon Energy ◽  
High Efficiency ◽  
High Energy ◽  
Yield Enhancement ◽  
X Ray ◽  
Simulation Results ◽  
Conversion Efficiencies

High-energy (>20 keV) X-ray photon detection at high quantum yield, high spatial resolution, and short response time has long been an important area of study in physics. Scintillation is a prevalent method but limited in various ways. Directly detecting high-energy X-ray photons has been a challenge to this day, mainly due to low photon-to-photoelectron conversion efficiencies. Commercially available state-of-the-art Si direct detection products such as the Si charge-coupled device (CCD) are inefficient for >10 keV photons. Here, we present Monte Carlo simulation results and analyses to introduce a highly effective yet simple high-energy X-ray detection concept with significantly enhanced photon-to-electron conversion efficiencies composed of two layers: a top high-Z photon energy attenuation layer (PAL) and a bottom Si detector. We use the principle of photon energy down conversion, where high-energy X-ray photon energies are attenuated down to ≤10 keV via inelastic scattering suitable for efficient photoelectric absorption by Si. Our Monte Carlo simulation results demonstrate that a 10–30× increase in quantum yield can be achieved using PbTe PAL on Si, potentially advancing high-resolution, high-efficiency X-ray detection using PAL-enhanced Si CMOS image sensors.

Polymer Loop Formation on a Functionalized Hard Surface: Quantitative Insight by Comparison of Experimental and Monte Carlo Simulation Results

Langmuir ◽  
2010 ◽  
Vol 26 (1) ◽  
pp. 202-209 ◽  
Author(s):  
Zhenyu Haung ◽  
Haining Ji ◽  
Jimmy Mays ◽  
Mark Dadmun ◽  
Grant Smith ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Loop Formation ◽  
Hard Surface ◽  
Simulation Results

scholarly journals Kinetic Monte Carlo Simulation of Clustering in an Al-Mg-Si-Cu Alloy

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4523
Author(s):  
Qilu Ye ◽  
Jianxin Wu ◽  
Jiqing Zhao ◽  
Gang Yang ◽  
Bin Yang
Keyword(s):  
Activation Energy ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
Complex Interaction ◽  
Cu Alloy ◽  
Cu Alloys ◽  
Simulation Results ◽  
Small Clusters ◽  
First Time

The mechanism of the clustering in Al-Mg-Si-Cu alloys has been a long-standing controversial issue. Here, for the first time, the mechanism of the clustering in the alloy was investigated by a Kinetic Monte Carlo (KMC) approach. In addition, reversion aging (RA) was carried out to evaluate the simulation results. The results showed that many small-size clusters formed rapidly in the early stages of aging. With the prolongation of aging time, the clusters merged and grew. The small clusters formed at the beginning of aging in Al-Mg-Si-Cu alloy were caused by initial vacancies (quenching vacancies). The merging and decomposition of the clusters were mainly caused by the capturing of vacancies, and the clusters had a probability to decompose before reaching a stable size. After repeated merging and decomposition, the clusters reach stability. During RA, the complex interaction between the cluster merging and decomposition leaded to the partial irregular change of the hardness reduction and activation energy.

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