scholarly journals Comparing the Thiessen’s Method against simpler alternatives using Monte Carlo Simulation

2018 ◽  
pp. 125-138
Author(s):  
Marcelo Guelfi ◽  
Carlos López-Vazquez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Bootstrap Method ◽  
Field Measurements ◽  
Expected Value ◽  
Systematic Analysis ◽  
Sample Mean ◽  
Friedman's Test ◽  
Data Location

Estimating the expected value of a function over geographic areas is problem with a long history. In the beginning of the XX-th century the most common method was just the arithmetic mean of the field measurements ignoring data location. In 1911, Thiessen introduced a new weighting procedure measuring influence through an area and thus indirectly considering closeness between them. In another context, Quenouville created in 1949 the jackknife method which is used to estimate the bias and the standard deviation. In 1979 Efron invented the bootstrap method which, among other things, is useful to estimate the expected value and the confidence interval (CI) from a population. Although the Thiessen’s method has been used for more than 100 years, we were unable to find systematic analysis comparing its efficiency against the simple mean, or even to more recent methods like jackknife or boostrap. In this work we compared four methods to estimate de expected value.  Sample mean, Thiessen, the so called here jackknifed Thiessen and bootstrap. All of them are feasible for routine use in a network of fixed locations. The comparison was made using the Friedman’s Test after a Monte Carlo simulation. Two cases were taken for study: one analytic with three arbitrary functions and the other using experimental data from daily rain measured with a satellite. The results show that Thiessen’s method is the best estimator in almost all the cases with a 95% of confidence interval. Unlike the others, the last two considered methods supply a suitable CI, but the one obtained through jackknifed Thiessen was even more accurate, opening the door for future work.

scholarly journals Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4965
Author(s):  
Kun Mo Lee ◽  
Min Hyeok Lee ◽  
Jong Seok Lee ◽  
Joo Young Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Emission Factor ◽  
Bootstrap Method ◽  
Ghg Emissions ◽  
Parametric Bootstrap ◽  
Random Variable ◽  
Ghg Emission ◽  
Nonparametric Bootstrap ◽  
Mcs Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

SU-FF-T-133: Commissioning Electron Beams with Monte Carlo Simulation Based On Large Field Measurements

2006 ◽  
Vol 33 (6Part8) ◽  
pp. 2079-2079
Author(s):  
B Faddegon ◽  
J Lehmann ◽  
J Chen ◽  
E Schreiber
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Electron Beams ◽  
Field Measurements ◽  
Large Field ◽  
Simulation Based

Bias and Precision of Alternate Estimators in Meta-Analysis: Benefits of Blending Schmidt-Hunter and Hedges Approaches

2017 ◽  
Vol 22 (2) ◽  
pp. 490-514 ◽  
Author(s):  
Michael T. Brannick ◽  
Sean M. Potter ◽  
Bryan Benitez ◽  
Scott B. Morris
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Root Mean Square Error ◽  
Random Effects ◽  
Meta Analysis ◽  
Size Distributions ◽  
Mean Square ◽  
The Mean

We describe a new estimator (labeled Morris) for meta-analysis. The Morris estimator combines elements of both the Schmidt-Hunter and Hedges estimators. The new estimator is compared to (a) the Schmidt-Hunter estimator, (b) the Schmidt-Hunter estimator with variance correction for the number of studies (“ k correction”), (c) the Hedges random-effects estimator, and (d) the Bonett unit weights estimator in a Monte Carlo simulation. The simulation was designed to represent realistic conditions faced by researchers, including population random-effects distributions, numbers of studies, and skewed sample size distributions. The simulation was used to evaluate the estimators with respect to bias, coverage of the 95% confidence interval of the mean, and root mean square error of estimates of the population mean. We also evaluated the quality of credibility intervals. Overall, the new estimator provides better coverage and slightly better credibility values than other commonly used methods. Thus it has advantages of both commonly used approaches without the apparent disadvantages. The new estimator can be implemented easily with existing software; software used in the study is available online, and an example is included in the appendix in the Supplemental Material available online.

Prediction and mitigation of construction noise in an urban environment

2003 ◽  
Vol 30 (4) ◽  
pp. 659-672 ◽  
Author(s):  
A Gilchrist ◽  
E N Allouche ◽  
D Cowan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Urban Areas ◽  
Deterministic Model ◽  
Field Measurements ◽  
Mitigation Strategies ◽  
Source Control ◽  
Construction Site ◽  
Noise Mitigation ◽  
Monte Carlo Simulation Technique

A growing number of construction projects are performed in congested urban areas. Often, the surrounding community finds these projects annoying because of noise, vibration, dust, light, and greenhouse gas emissions. This paper focuses on one type of irritant, noise. Common noise generators on construction sites are identified, and the elements of a generic program for mitigating construction-related noise are outlined. Mitigation strategies including source control, path control, and receiver control are discussed. A deterministic model based on the Monte Carlo simulation technique is used. It is capable of predicting the magnitude and frequency of noise levels generated by construction equipment at receptor locations around a construction site during each construction stage. The utilization of the model as a planning tool for optimizing the composition, geometry, and location of noise barriers around a construction site is demonstrated via a case history, namely the construction of an eight-storey parking garage in London, Ont. The model is validated by comparing its predictions to field measurements undertaken during various construction stages. Predictions agree favourably with field measurements.Key words: construction, noise, mitigation, barriers, modeling, Monte Carlo simulation.

scholarly journals Optimal Control Methods of Experiment Times in System-of-Systems Combat Computer Simulation

2019 ◽  
Vol 26 ◽  
pp. 03004
Author(s):  
Zhiqiang Zhao ◽  
Feiyue Zhou
Keyword(s):  
Computer Simulation ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Simulation Experiment ◽  
Random Factor ◽  
Interval Method ◽  
Interval Extension ◽  
Self Adaptation ◽  
Optimal Control Methods

In the process of scheme optimization, in order to eliminate the influence of random factor, it needs to conduct computer simulation of Monte Carlo. Therefore, it is proposed to introduce confidence interval into systemof-systems combat simulation, and confirm whether the Monte Carlo simulation finishes according to data sample generated in simulation process. According to characteristic of data sample, extend correspondingly confidence interval method, and under the condition of obtaining the solution meeting accuracy requirements, reduce simulation experiment times as far as possible. The simulation experiment results show that confidence interval extension method is able to possess self-adaptation control to Monte Carlo simulation.

scholarly journals Multiplicative Gains, Non-ergodic Utility, and the Just One More Paradox (with Supplemental Information)

2021 ◽  
Author(s):  
John K. Myers
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Expected Value ◽  
Multiplicative Semigroup ◽  
The Real ◽  
Operator Notation ◽  
Multiplicative Problems ◽  
Utility Preferences ◽  
Continuous Operators

Abstract Interest in multiplicative vs. additive returns on bets has been revived by Peters, who proposes ergodicity and added noise are useful in understanding utility preferences. Peters requires a Monte Carlo simulation to demonstrate empirically a supposed paradox that arithmetic expectation is inappropriate for multiplicative gain distribution forecasting. Here I formalize the r operator notation, which significantly simplifies multiplicative problems, as an extension of the arithmetic group's Δ/d discrete and continuous operators into the multiplicative semigroup. I show how the annihilating (absorbing) element of the multiplicative semigroup at 0, not +/-∞, may be used to conveniently represent nonlinear sequence occurrences, such as running out of money, without the need for special computer rules outside the mathematics. I use this to solve Peters' expected-value paradox elegantly, without ergodicities nor noise. But Peters misses the real paradox, called “Just One More”: the outcome of an advantageous additive gamble is identical to the outcome of a similar disadvantageous multiplicative gamble, after one trial; hence, by induction, an agent will keep playing. I propose games “Hero or Heroin” and “American Roulette” to highlight this paradox. This may help in explaining addiction. The Supplement contains further visualizations and arguments against the need and applicability of ergodics for utility. The results contribute to the understanding of repeated multiplicative gambles with annihilating states, and their utility.

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