scholarly journals A Monte Carlo simulation to validate the EAR cut-point method for assessing the prevalence of nutrient inadequacy at the population level

2004 ◽  
Vol 7 (7) ◽  
pp. 893-900 ◽  
Author(s):  
B de Lauzon ◽  
JL Volatier ◽  
A Martin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Population Level ◽  
Point Method ◽  
Probabilistic Methods ◽  
Recommended Dietary Allowance ◽  
The Real ◽  
Cut Point ◽  
Probability Approach ◽  
Dietary Allowance

AbstractObjective:The aim of this study was to validate the EAR cut-point method for assessing the prevalence of nutrient inadequacy at the population level.Design and subjects:Different methods for estimating the prevalence of inadequate intake were compared: the cut-off point method, with cut-off points at the Recommended Dietary Allowance (RDA), 0.66 RDA, 0.50 RDA and the Estimated Average Requirement (EAR); the probability approach; and a Monte Carlo simulation. In total, 591 men and 674 women, aged 20–55 years, were included in the analyses.Results:The prevalence of inadequate intake as estimated by the EAR cut-point method was similar to the prevalence of inadequacy estimated by both probabilistic methods. The cut-point method with RDA, 0.66 RDA and 0.50 RDA as cut-off limits induced an over- or an underestimation of the real prevalence of inadequacy.Conclusions:Probabilistic methods consider both the intake variability and the requirement variability, and, as a result, their estimation should be closer to the real prevalence of inadequacy. The use of the EAR cut-point method yields a good estimation of the prevalence of inadequate intake, comparable to the probability approach, and limits over- and underestimation of the prevalence induced by other cut-off points.

scholarly journals Comparison of the Datar-Mathews Method and the Fuzzy Pay-Off Method through Numerical Results

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Mariia Kozlova ◽  
Mikael Collan ◽  
Pasi Luukka
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Real Option ◽  
Fuzzy Numbers ◽  
Numerical Results ◽  
Option Valuation ◽  
The Real ◽  
Valuation Methods ◽  
Real Option Valuation

The paper compares numerically the results from two real option valuation methods, the Datar-Mathews method and the fuzzy pay-off method. Datar-Mathews method is based on using Monte Carlo simulation within a probabilistic valuation framework, while the fuzzy pay-off method relies on modeling the real option valuation by using fuzzy numbers in a possibilistic space. The results show that real option valuation results from the two methods seem to be consistent with each other. The fuzzy pay-off method is more robust and is also usable when not enough information is available for a construction of a simulation model.

Circular Arc Gear Reliability Study Based on Modified FOSM Method

2013 ◽  
Vol 760-762 ◽  
pp. 2216-2219
Author(s):  
Zhong Li ◽  
Bo Yu Cheng
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Limit State ◽  
Point Method ◽  
Reliability Study ◽  
Circular Arc ◽  
Gear Case ◽  
Improve Reliability ◽  
State Functions

As different limit state functions are used to analyze reliability, there is a great distinctness among the calculated results. In this paper an improved LOSM method is proposed, namely, checking point method. The circular arc gear case is employed to demonstrate this method. In contrast to the results of Monte Carlo simulation, this method can greatly improve reliability calculations precision.

Project Scheduling Problem with Stochastic Resource-Dependent Activity Duration

2013 ◽  
Vol 483 ◽  
pp. 607-610 ◽  
Author(s):  
Chun Jie Zhong ◽  
Ying Yu ◽  
Yun Lang Jia
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Project Scheduling ◽  
Point Method ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Activity Duration ◽  
Dependent Activity ◽  
Project Scheduling Problem

A resource-constrained project scheduling problem with stochastic resource-dependent activity durations is presented in this paper,and the two-point method is employed to simulate the uncertain property.Furthermore a genetic algorithm combined with this method is provided to solve the problem. Compared with the results from the genetic with Monte Carlo simulation, the proposed method is verified to be effective and more efficient.

scholarly journals Nutritional Inadequacy: Unraveling the Methodological Challenges for the Application of the Probability Approach or the EAR Cut-Point Method—A Pregnancy Perspective

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3473
Author(s):  
Foteini Tsakoumaki ◽  
Charikleia Kyrkou ◽  
Apostolos P. Athanasiadis ◽  
Georgios Menexes ◽  
Alexandra-Maria Michaelidou
Keyword(s):  
Simulated Data ◽  
Point Method ◽  
Final Decision ◽  
Bootstrap Confidence Intervals ◽  
Cut Point ◽  
Probability Approach ◽  
Methodological Challenges ◽  
Usual Intake ◽  
Detailed Statistical Analysis ◽  
Method Point

The aim of this study was to unravel the methodological challenges when exploring nutritional inadequacy, involving 608 healthy pregnant women. The usual intake of twenty-one nutrients was recorded by employing a validated FFQ. Simulated datasets of usual intake were generated, with randomly imposed uncertainty. The comparison between the usual intake and the EAR was accomplished with the probability approach and the EAR cut-point method. Point estimates were accompanied by bootstrap confidence intervals. Bootstrap intervals applied on the risk of inadequacy for raw and simulated data tended in most cases to overlap. A detailed statistical analysis, aiming to predict the level of inadequacy, as well as the application of the EAR cut-point method, along with bootstrap intervals, could effectively be used to assess nutrient inadequacy. However, the final decision for the method used depends on the distribution of nutrient-intake under evaluation. Irrespective of the applied methodology, moderate to high levels of inadequacy, calculated from FFQ were identified for certain nutrients (e.g. vitamins C, B6, magnesium, vitamin A), while the highest were recorded for folate and iron. Considering that micronutrient-poor, obesogenic diets are becoming more common, the underlying rationale may help towards unraveling the complexity characterizing nutritional inadequacies, especially in vulnerable populations.

geometry of Venice

2004 ◽  
Vol 1 (1) ◽  
pp. 265-275
Author(s):  
Luigi Calzavara ◽  
Maurizio Brizzi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Null Hypothesis ◽  
Poisson Model ◽  
Probabilistic Methods ◽  
Simple Index ◽  
Original Group ◽  
Random Location

Having observed that ancient Venice belfries are located in such a way that they generate many Pythagorean triangles, having a great number of vertices in common, it has been decided to test the null hypothesis of random location by statistical and probabilistic methods. A simple index, called Pythagorean Ratio, is proposed, for checking which triangles are to be considered as Pythagorean. Then, a Monte Carlo simulation is performed, generating samples of "random belfries" in the historical kernel of Venice; a Poisson model seems to fit very well the number X of Pythagorean triangles. Combining this number with the number of connections, the null hypothesis is rejected. Adding a further belfry (S.Simeon Grande) to the original group of belfries, the significance becomes even higher.

Stochastic Applications in Crashworthiness

2001 ◽  
Author(s):  
Rama P. Koganti ◽  
Ari G. Caliskan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Yield Strength ◽  
Extreme Values ◽  
Empirical Relation ◽  
Outer Radius ◽  
Probabilistic Methods ◽  
Stochastic Methods ◽  
Element Analysis ◽  
The Mean

Abstract Due to geometrical, and material property variations, response of any structural member varies from the nominal design value. Typically the geometrical and material variations are the resultant of manufacturing variations. In this paper, the effect of these variations about the nominal values on structural response is studied using stochastic or probabilistic methods. Circular aluminum cross-sections are becoming popular in structural energy management applications. Also, significant research has been done to estimate the mean crush load for a circular section using empirical relations. An empirical relation, which is a function of thickness, outer radius, elastic modulus and yield strength, was used to estimate the mean crush load. Based on the measured thickness, outer radius and yield strength, the mean crush load is calculated using the empirical relation. Also, using the empirical relation, the variation in the mean crush load is estimated using linear statistical approach and Monte-Carlo simulation. In both the stochastic methods, actual mean and standard deviations of thickness, outer radius and yield strength are used. Also, using the extreme variations of these factors, mean crush load is predicted using an implicit Finite Element Analysis (FEA) code. The FEA prediction is in good agreement with the results of the testing. However, the designed mean crush load based on the empirical relation overestimates the crush loads by about 11%. The results of the study showed that the tube thickness and yield strength variations significantly affect the crush loads. Based on the Monte-Carlo simulation and FEA values using the extreme values for the geometrical and mechanical properties, one can design crash structures that take into account the inherent variability of components.

scholarly journals Monte Carlo study of the frame, fluctuation and internal tensions of fluctuating membranes with fixed area

Soft Matter ◽  
2016 ◽  
Vol 12 (8) ◽  
pp. 2373-2380 ◽  
Author(s):  
Hayato Shiba ◽  
Hiroshi Noguchi ◽  
Jean-Baptiste Fournier
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Lipid Membranes ◽  
Monte Carlo Study ◽  
Fluctuation Spectrum ◽  
Projected Area ◽  
Membrane Area ◽  
The Real ◽  
Lattice Monte Carlo ◽  
Fixed Area

Three types of surface tensions are investigated for lipid membranes using a lattice Monte Carlo simulation: the internal tension,σ, conjugated to the real membrane area, the mechanical frame tension,τ, conjugated to the projected area, and the “fluctuation tension”,r, obtained from the height fluctuation spectrum.

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.

Sign in / Sign up

Export Citation Format

Share Document