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Relevant bibliographies by topics / Monte Carlo Simulation, Separable Monte Carlo Simulation, Bootstrapping

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Academic literature on the topic 'Monte Carlo Simulation, Separable Monte Carlo Simulation, Bootstrapping'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Monte Carlo Simulation, Separable Monte Carlo Simulation, Bootstrapping"

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Jehan, Musarrat, and Efstratios Nikolaidis. "Bootstrapping and Separable Monte Carlo Simulation Methods Tailored for Efficient Assessment of Probability of Failure of Structural Systems." SAE International Journal of Materials and Manufacturing 8, no. 3 (April 14, 2015): 609–15. http://dx.doi.org/10.4271/2015-01-0420.

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Ben Seghier, Mohamed el Amine, Mourad Bettayeb, José Correia, Abílio De Jesus, and Rui Calçada. "Structural reliability of corroded pipeline using the so-called Separable Monte Carlo method." Journal of Strain Analysis for Engineering Design 53, no. 8 (June 22, 2018): 730–37. http://dx.doi.org/10.1177/0309324718782632.

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The evaluation of the failure probability of corroded pipelines is an important calculation to quantify the risk assessment and integrity of pipelines. Traditional Monte Carlo simulation method has been widely used to solve this type of problems, where it generates a very large number of simulations and takes longer time in computing. In this study, enhanced computational method called Separable Monte Carlo is employed to evaluate the time-dependent reliability of pipeline segments containing active corrosion defects, where a practical example was used. The results show that the Separable Monte Carlo simulation method not only minimizes the computational cost strongly but also improves the calculation precision.

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Wayne Oldford, R. "Bootstrapping by monte carlo versus approximating the estimator and bootstrapping exactly: Cost and performance." Communications in Statistics - Simulation and Computation 14, no. 2 (January 1985): 395–424. http://dx.doi.org/10.1080/03610918508812446.

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Wang, Yunfei, and Dejian Lai. "ASSESSING LOGISTIC REGRESSION BY BOOTSTRAPPING AND MONTE CARLO SIMULATION: MODELING LOW BIRTH WEIGHT." JP Journal of Biostatistics 16, no. 2 (July 10, 2019): 13–30. http://dx.doi.org/10.17654/bs016020013.

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Létourneau, Pascal, and Lars Stentoft. "Bootstrapping the Early Exercise Boundary in the Least-Squares Monte Carlo Method." Journal of Risk and Financial Management 12, no. 4 (December 15, 2019): 190. http://dx.doi.org/10.3390/jrfm12040190.

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This paper proposes an innovative algorithm that significantly improves on the approximation of the optimal early exercise boundary obtained with simulation based methods for American option pricing. The method works by exploiting and leveraging the information in multiple cross-sectional regressions to the fullest by averaging the individually obtained estimates at each early exercise step, starting from just before maturity, in the backwards induction algorithm. With this method, less errors are accumulated, and as a result of this, the price estimate is essentially unbiased even for long maturity options. Numerical results demonstrate the improvements from our method and show that these are robust to the choice of simulation setup, the characteristics of the option, and the dimensionality of the problem. Finally, because our method naturally disassociates the estimation of the optimal early exercise boundary from the pricing of the option, significant efficiency gains can be obtained by using less simulated paths and repetitions to estimate the optimal early exercise boundary than with the regular method.

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Lee, Hyo-Nam, and Jong-Yun Oh. "Monte Carlo Simulation on Reliability of a Self-Separable Ejector for Man-Portable Missiles." International Journal of Aeronautical and Space Sciences 12, no. 4 (December 30, 2011): 385–95. http://dx.doi.org/10.5139/ijass.2011.12.4.385.

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Tinungki, Georgina M. "Penentuan Distribusi Sample Terbatas Uji-J Davidson dan Mackinnon dengan Metode Bootstrap pada Model Regresi Tak Tersarang." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (December 20, 2018): 1. http://dx.doi.org/10.20956/jmsk.v15i2.5708.

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Georgina Maria Tinungki* AbstractThere are some tests proposed for un-nested hypothesis between J-Davidson Test and MacKinnon Test. J’s Test is often bad result, but it always works very well when used bootstrap. Bootstrapping for J’s Test is expected to be able to show that by using bounded sample is better, because there is no fault in counting process. Moreover, bootstrapping J-Test will omit the possibility of inconsistence of the results test previously. Simulation result of Monte Carlo will compare the proposed bounded sample test with Cox and J’s Test previously. Keywords: un-nested hypothesis, J-Davidson Test, MacKinnon Test AbstrakTerdapat beberapa pengujian yang diusulkan untuk hipotesis tak tersarang antara lain Uji-J Davidson dan MacKinnon. Uji-J sering bekerja buruk, tetapi biasanya bekerja sangat baik ketika dibootstrapkan.. Bootstrapping Uji-J diharapkan mampuh menunjukkan sampel terbatas lebih baik karena tidak mempunyai kesalahan didalam proses perhitungan. Lebih dari itu, bootstrapping J-Tests akan mengeluarkan kemungkinan dari ketidak konsistenan hasil uji yang sebelumnya. Hasil Simulasi Monte Carlo membandingkan uji sampel terbatas yang diusulkan dengan test yang sebelumnya seperti Uji Cox dan J-Test. Kata Kunci: Hipotesis tak tersarang,, Uji-J Davidson, Uji MacKinnon

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Oppe, Mark, Daniela Ortín-Sulbarán, Carlos Vila Silván, Anabel Estévez-Carrillo, and Juan M. Ramos-Goñi. "Cost-effectiveness of adding Sativex® spray to spasticity care in Belgium: using bootstrapping instead of Monte Carlo simulation for probabilistic sensitivity analyses." European Journal of Health Economics 22, no. 5 (April 20, 2021): 711–21. http://dx.doi.org/10.1007/s10198-021-01285-1.

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Abstract Background Uncertainty in model-based cost-utility analyses is commonly assessed in a probabilistic sensitivity analysis. Model parameters are implemented as distributions and values are sampled from these distributions in a Monte Carlo simulation. Bootstrapping is an alternative method that requires fewer assumptions and incorporates correlations between model parameters. Methods A Markov model-based cost–utility analysis comparing oromucosal spray containing delta-9-tetrahidrocannabinol + cannabidiol (Sativex®, nabiximols) plus standard care versus standard spasticity care alone in the management of multiple sclerosis spasticity was performed over a 5-year time horizon from the Belgian healthcare payer perspective. The probabilistic sensitivity analysis was implemented using a bootstrap approach to ensure that the correlations present in the source clinical trial data were incorporated in the uncertainty estimates. Results Adding Sativex® spray to standard care was found to dominate standard spasticity care alone, with cost savings of €6,068 and a quality-adjusted life year gain of 0.145 per patient over the 5-year analysis. The probability of dominance increased from 29% in the first year to 94% in the fifth year, with the probability of QALY gains in excess of 99% for all years considered. Conclusions Adding Sativex® spray to spasticity care was found to dominate standard spasticity care alone in the Belgian healthcare setting. This study showed the use of bootstrapping techniques in a Markov model probabilistic sensitivity analysis instead of Monte Carlo simulations. Bootstrapping avoided the need to make distributional assumptions and allowed the incorporation of correlating structures present in the original clinical trial data in the uncertainty assessment.

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Olaniran, Oyebayo Ridwan, and Mohd Asrul Affendi Abdullah. "Bayesian Analysis of Extended Cox Model with Time-Varying Covariates Using Bootstrap Prior." Journal of Modern Applied Statistical Methods 18, no. 2 (July 17, 2020): 2–13. http://dx.doi.org/10.22237/jmasm/1604188980.

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A new Bayesian estimation procedure for extended cox model with time varying covariate was presented. The prior was determined using bootstrapping technique within the framework of parametric empirical Bayes. The efficiency of the proposed method was observed using Monte Carlo simulation of extended Cox model with time varying covariates under varying scenarios. Validity of the proposed method was also ascertained using real life data set of Stanford heart transplant. Comparison of the proposed method with its competitor established appreciable supremacy of the method.

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Kimura, Daniel K. "Approaches to Age-Structured Separable Sequential Population Analysis." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 12 (December 1, 1990): 2364–74. http://dx.doi.org/10.1139/f90-263.

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Modern statistical methods are being used more often to perform age-structured separable sequential population analysis (SSPA). This paper describes how some of these methods can be easily understood from a unified point of view. The approach is to begin with the now standard separable age-structured model, and modify some of the basic assumptions. The resulting models are examined using Monte Carlo simulation, with the mean square error of modeled biomass estimates used as the evaluation criterion. Simulation results indicate that nonlinear least squares and multinomial maximum likelihood are both capable of fitting lognormally and multinomially distributed catch-at-age data. It also appears that errors in modeling results introduced by ageing error may be minor, provided ageing error is of modest magnitude and is normally distributed. However, use of a somewhat incorrect functional form for the selectivities can cause greatly increased error in the modeling results, indicating that caution should be exercised when modeling selectivities. Results indicate that length-based SSPA is feasible. And finally, the models are used to provide insight into the old question of "how many fish should be aged?"

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Dissertations / Theses on the topic "Monte Carlo Simulation, Separable Monte Carlo Simulation, Bootstrapping"

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Ugwumba, Miracle C. "Reliability Assessment Using Bootstrapping and Identification of Point of Diminishing Returns." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1452080021.

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Jehan, Musarrat. "Bootstrapping & Separable Monte Carlo Simulation Methods Tailored for Efficient Assessment of Probability of Failure of Dynamic Systems." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1418161683.

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Callert, Gustaf, and Dahlström Filip Halén. "A performance investigation and evaluation of selected portfolio optimization methods with varying assets and market scenarios." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190997.

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This study investigates and evaluates how different portfolio optimization methods perform when varying assets and financial market scenarios. Methods included are mean variance, Conditional Value-at-Risk, utility based, risk factor based and Monte Carlo optimization. Market scenarios are represented by stagnating, bull and bear market data from the Bloomberg database. In order to perform robust optimizations resampling of the Bloomberg data has been done hundred times. The evaluation of the methods has been done with respect to selected ratios and two benchmark portfolios. Namely an equally weighted portfolio and an equally weighted risk contributions portfolio. The study found that mean variance and Conditional Value-at-Risk optimization performed best when using linear assets in all the investigated cases. Considering non-linear assets such as options an equally weighted portfolio performs best.
Den här studien undersöker och utvärderar hur olika portföljoptimeringsmetoder presterar med varierande finansiella tillgångsslag och marknadsscenarion. De metoder som har undersökts är: väntevärde-varians, villkorligt-värde-av-risk, nyttjande- och Monte Carlo baserad optimering. De marknadsscenarion som valts är: stagnerande, uppåt- samt nedåtgående scenarion där marknadsdata hämtats från Bloomberg för respektive tillgång. För att erhålla robusta optimeringsresultat har data omsamplats hundra gånger. Utvärderingen av metoderna har gjorts med avseende på utvalda indikatorer och två jämförelseportföljer, en likaviktad portfölj och en likariskviktad portfölj. Studien fann att portföljer genererade av väntevärde-varians och villkorligt-värde-av-risk optimering visade bäst prestanda, när linjära tillgångar använts i samtliga scenarion. När ickelinjära tillgångar såsom optioner har använts gav den likaviktade jämförelseportföljen bäst resultat i samtliga scenarion.

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Book chapters on the topic "Monte Carlo Simulation, Separable Monte Carlo Simulation, Bootstrapping"

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"Monte Carlo Simulation and IBM SPSS Bootstrapping." In SPSSreg Statistics for Data Analysis and Visualization, 43–69. Indianapolis, Indiana: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119183426.ch2.

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Conference papers on the topic "Monte Carlo Simulation, Separable Monte Carlo Simulation, Bootstrapping"

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Smarslok, Benjamin, Dylan Alexander, Raphael Haftka, Laurent Carraro, and David Ginsbourger. "Separable Monte Carlo Simulation Applied to Laminated Composite Plates Reliability." In 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
16th AIAA/ASME/AHS Adaptive Structures Conference
10t. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-1751.

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Ravishankar, Bharani, Benjamin Smarslok, Raphael Haftka, and Bhavani Sankar. "Separable Sampling of the Limit State for Accurate Monte Carlo Simulation." In 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-2266.

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Chaudhuri, Anirban, and Raphael T. Haftka. "Combining Separable Monte Carlo With Importance Sampling for Improved Accuracy." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48650.

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Monte-Carlo (MC) methods are often used to carry out reliability based design of structures. Methods that improve the accuracy of MC simulation include Separable Monte Carlo (SMC), Markov Chain Monte-Carlo, and importance sampling. We explore the utility of combining SMC and importance sampling for improving accuracy. The accuracy of the estimates is compared for crude MC, SMC, importance sampling and combined method for a composite plate example and a tuned mass damper example. For these examples SMC and importance sampling reduced the error individually by factors of 2 to 5, and the combination reduced it further by about a factor of 2. The results were also compared with the first order reliability method (FORM). FORM was grossly inaccurate for the tuned mass-damper example which has a failure region bounded by safe regions on either side.

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Prots, Andriy, Lars Högner, Matthias Voigt, Ronald Mailach, and Florian Danner. "Improved Quality Assessment of Probabilistic Simulations and Application to Turbomachinery." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16147.

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Abstract Probabilistic methods are gaining in importance in aerospace engineering due to their ability to describe the behavior of the system in the presence of input value variance. A frequently employed probabilistic method is the Monte Carlo Simulation (MCS). There, a sample of random representative realizations is evaluated deterministically and their results are afterwards analyzed with statistical methods. Possible statistical results are mean, standard deviation, quantile values and correlation coefficients. Since the sample is generated randomly, the result of a MCS will differ for each repetition. Therefore, it can be regarded as a random variable. Confidence Intervals (CIs) are commonly used to quantify this variance. To gain the true CI, many repetitions of the MCS have to be conducted, which is not desirable due to limitations in time and computational power. Hence, analytical formulations or bootstrapping is used to estimate the CI. In order to reduce the variance of the result of a MCS, sampling techniques with variance reduction properties like Latin Hypercube Sampling (LHS) are commonly used. But the known methods to determine the CI do not consider this variance reduction and tend to overestimate it instead. Furthermore, it is difficult to predict the change of the CI size with increasing size of the sample. In the present work, new methods to calculate the CI are introduced. They allow a more precise CI estimation when LHS is used for a MCS. For this purpose, the system is approximated by means of a meta model. The distribution of the result value is now approximated by repeating the MCS many times. The time consuming deterministic calculations of a MCS are thus replaced with an evaluation on the meta model. These so called virtual MCS can therefore be performed in a short amount of time. The estimated distribution of the result value can be used to estimate the CI. It is, however, not sufficient to use only the meta model. The error ε, defined as the difference between the true value y and the approximated value y, must be considered as well. The generated meta model can also be used to predict the size of the CI at different sample sizes. The suggested methods were applied to two test cases. The first test case examines a structural mechanics application of a bending beam, which features low computational cost. This allows to show that the predicted sizes of the CI are sufficiently precise. The second test case covers the aerodynamic application. Therefore, an aerodynamic Computational Fluid Dynamics (CFD) analysis accounting for geometrical variations of NASA’s Rotor 37 is conducted. For this, the blade is parametrized with the in-house tool Blade2Parameter. For different sample sizes, blades are generated using this parametrization. Their geometrical variance is based on experience values. CFD calculations for these blades are performed with the commercial software NUMECA. Afterwards, the CIs for result values of interest like mechanical efficiency are evaluated with the presented methods. The suggested methods predict a narrower and thus less conservative CI.

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