Suchergebnisse

In this paper we describe a simulation, by Monte Carlo methods, of the results of rodent carcinogenicity bioassays. Our aim is to study how the observed correlation between carcinogenic potency (β or ln2/TD50) and maximum tolerated dose (MTD) arises, and whether the existence of this correlation leads to an artificial correlation between carcinogenic potencies in rats and mice. The validity of the bioassay results depends upon, among other things, certain biases in the experimental design of the bioassays. These include selection of chemicals for bioassay and details of the experimental protocol, including dose levels. We use as variables in our simulation the following factors: (1) dose group size, (2) number of dose groups, (3) tumor rate in the control (zero‐dose) group, (4) distribution of the MTD values of the group of chemicals as specified by the mean and standard deviation, (5) the degree of correlation between β and the MTD, as given by the standard deviation of the random error term in the linear regression of log β on log (1/MTD), and (6) an upper limit on the number of animals with tumors. Monte Carlo simulation can show whether the information present in the existing rodent bioassay database is sufficient to reject the validity of the proposed interspecies correlations at a given level of stringency. We hope that such analysis will be useful for future bioassay design, and more importantly, for discussion of the whole NCI/ NTP program.

Ferroelectrics find broad applications, e.g. in non-volatile memories, but the switching kinetics in real, disordered, materials is still incompletely understood. Here, we develop an electrostatic model to study ferroelectric switching using 3D Monte Carlo simulations. We apply this model to the prototypical small molecular ferroelectric trialkylbenzene-1,3,5-tricarboxamide (BTA) and find good agreement between the Monte Carlo simulations, experiments, and molecular dynamics studies. Since the model lacks any explicit steric effects, we conclude that these are of minor importance. While the material is shown to have a frustrated antiferroelectric ground state, it behaves as a normal ferroelectric under practical conditions due to the large energy barrier for switching that prevents the material from reaching its ground state after poling. We find that field-driven polarization reversal and spontaneous depolarization have orders of magnitude different switching kinetics. For the former, which determines the coercive field and is relevant for data writing, nucleation occurs at the electrodes, whereas for the latter, which governs data retention, nucleation occurs at disorder-induced defects. As a result, by reducing the disorder in the system, the polarization retention time can be increased dramatically while the coercive field remains unchanged. ; Funding Agencies|Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [2009 00971]; Vetenskapsradet; SeRC (Swedish e-Science Research Center)

PurposeTo address formally the issue of uncertainty in valuing real estate.Design/methodology/approachMonte Carlo simulations are used to incorporate the uncertainty of valuation parameters. The probability distributions of the various parameters are constructed using empirical data and a simple model is suggested to compute the discount rate.FindingsThe central values of the simulations are in most cases slightly less than the hedonic values. The confidence intervals are found to be most sensitive to the long‐term equilibrium interest rate being used and to the expected growth rate of the terminal value.Research limitations/implicationsFurther research should focus on the stability of the model when other portfolios are used and for different periods of the real estate cycle. It would also be fruitful to dig deeper in the relation between capital expenses and property values.Practical implicationsRisk can be assessed by valuers as they can measure the probability that the value of a property be less than a given threshold.Originality/valueBy incorporating uncertainty, the analysis does not yield merely a point estimate of the property's value but rather the entire distribution of values. Also this paper constitutes a contribution to the debate about valuation variation and the margin of error in valuing properties.

Simulation and Monte Carlo; Contents; Preface; Glossary; 1 Introduction to simulation and Monte Carlo; 2 Uniform random numbers; 3 General methods for generating random variates; 4 Generation of variates from standard distributions; 5 Variance reduction; 6 Simulation and finance; 7 Discrete event simulation; 8 Markov chain Monte Carlo; 9 Solutions; Appendix 1: Solutions to problems in Chapter 1; Appendix 2: Random number generators; Appendix 3: Computations of acceptance probabilities; Appendix 4: Random variate generators (standard distributions); Appendix 5: Variance reduction.

Dieses Buch ist ein handlicher und praktischer Leitfaden zur Monte Carlo Simulation. Er gibt eine Einführung in Standardmethoden und fortgeschrittene Verfahren, um die zunehmende Komplexität derivativer Portfolios besser zu erfassen. Das hier behandelte Spektrum reicht von der Preisbestimmung komplexerer Derivate, wie z.B. amerikanischer und asiatischer Optionen, bis hin zur Messung des Value at Risk und zur Modellierung komplexer Marktdynamik. Mit einer Vielzahl praktischer Beispiele und mit Hinweisen zu neuen Spitzentechniken. Die Autoren sind Experten auf dem Gebiet der Monte Carlo Simulation (MCS) und verfügen über langjährige Erfahrung im Umgang mit MCS-Methoden. Die Begleit-CD enthält alle im Buch beschriebenen Beispiele, mit denen der Leser frei experimentieren kann. "Monte Carlo Methods in Finance" - ein unverzichtbares Nachschlagewerk für quantitative Analysten, die bei der Bewertung von Optionspreisen und Risikomanagement auf Modelle zurückgreifen müssen.

Abstract
In this paper, an attempt is made to assessment and comparison of bootstrap experiment and Monte Carlo experiment for stock price simulation. Since the stock price evolution in the future is extremely important for the investors, there is the attempt to find the best method how to determine the future stock price of BNP Paribas′ bank. The aim of the paper is define the value of the European and Asian option on BNP Paribas′ stock at the maturity date. There are employed four different methods for the simulation. First method is bootstrap experiment with homoscedastic error term, second method is blocked bootstrap experiment with heteroscedastic error term, third method is Monte Carlo simulation with heteroscedastic error term and the last method is Monte Carlo simulation with homoscedastic error term. In the last method there is necessary to model the volatility using econometric GARCH model. The main purpose of the paper is to compare the mentioned methods and select the most reliable. The difference between classical European option and exotic Asian option based on the experiment results is the next aim of tis paper.

PurposeAlthough very significant and applicable, there have been no formal justifications for the use of Monte‐Carlo models and Markov chains in evaluating hospital admission decisions or concrete data supporting their use. For these reasons, this research was designed to provide a deeper understanding of these models. The purpose of this paper is to examine the usefulness of a computerized Monte‐Carlo simulation of admission decisions under the constraints of emergency departments.Design/methodology/approachThe authors construct a simple decision tree using the expected utility method to represent the complex admission decision process terms of quality adjusted life years (QALY) then show the advantages of using a Monte‐Carlo simulation in evaluating admission decisions in a cohort simulation, using a decision tree and a Markov chain.FindingsAfter showing that the Monte‐Carlo simulation outperforms an expected utility method without a simulation, the authors develop a decision tree with such a model. real cohort simulation data are used to demonstrate that the integration of a Monte‐Carlo simulation shows which patients should be admitted.Research limitations/implicationsThis paper may encourage researchers to use Monte‐Carlo simulation in evaluating admission decision implications. The authors also propose applying the model when using a computer simulation that deals with various CVD symptoms in clinical cohorts.Originality/valueAside from demonstrating the value of a Monte‐Carlo simulation as a powerful analysis tool, the paper's findings may prompt researchers to conduct a decision analysis with a Monte‐Carlo simulation in the healthcare environment.

AbstractAnalysis of cyclic‐linked cargo‐handling systems is presented and solved by Monte Carlo techniques for several typical instances.

The Air Force estimates military construction (MILCON) costs early in a project's development as a part of the funding approval process. However, many of the initial cost estimates deviate significantly more than expected from the actual project costs, hindering funding allocation efforts. There is a need for improved estimation techniques. This research examines a cost estimation model for the initial programming stages of a project when only general scope information is available. This study develops a Monte Carlo simulation based on historical construction cost data to predict project costs base on facility type. For a given facility type, the research identified distributions and associated correlations to model major cost elements from the historical data. The Monte Carlo simulation uses these distributions and correlations to estimate the total cost of separate validation projects. The results reveal a histogram, showing the probability range of possible costs for each project. This research compares these results to the actual costs and cost estimates for the same projects along with additional estimated costs derived from standard Air Force cost estimation guides. The results highlight the level of accuracy for current estimation techniques and validate the utility of this model. The Air Force can use this model to improve initial cost estimates, better predicting expected costs in addition to revealing the uncertainty inherent in those costs.