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Books on the topic 'Analyse monte carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Conjoint-Analyse mit vielen Merkmalen: Monte Carlo-Untersuchung einer gebrückten Conjoint-Analyse. P. Lang, 2002.

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2

Hillig, Thomas. Verfahrensvarianten der Conjoint-Analyse zur Prognose von Kaufentscheidungen: Eine Monte-Carlo-Simulation. Deutscher Universitäts-Verlag, 2006.

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3

Weber, Marcus. Meshless methods in conformation dynamics. Hut, 2006.

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4

Bishop, Keller Herbert, ed. Analysis of numerical methods. Dover Publications, 1994.

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5

Novak, Erich. Deterministic and stochastic error bounds in numerical analysis. Springer-Verlag, 1988.

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6

Lemieux, Christiane. Monte carlo and quasi-monte carlo sampling. Springer, 2009.

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7

Henryk, Woźniakowski, and SpringerLink (Online service), eds. Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer Berlin Heidelberg, 2012.

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8

1968-, Keller Alexander, Heinrich Stefan, and Niederreiter Harald 1944-, eds. Monte Carlo and Quasi-Monte Carlo methods 2006. Springer, 2008.

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9

Liang, F. Advanced Markov chain Monte Carlo methods: Learning from past samples. Wiley, 2010.

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10

Haug, Alfred A. Tests for cointegration: a Monte Carlo comparison. York University, Department of Economics, 1993.

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11

Heitmueller, Axel. A note on decompositions in fixed effects models in the presence of time-invariant characteristics. IZA, 2005.

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12

József, Gaál. Elektronikus áramkörök statisztikus analízise és szintézise Monte-Carlo módszerrel. Akadémiai Kiadó, 1987.

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13

Evans, Michael J. Monte Carlo computation of marginal posterior qualities. University of Toronto, Dept. of Statistics, 1988.

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14

Jansen, Jan Theo Maria. Monte Carlo calculations in diagnostic radiology: Dose conversion factors and risk benefit analyses = Monte Carlo berekeningen in de radiodiagnostiek : dosis conversiefactoren en risico baten analyses. Rijkuniversiteit te Leiden, 1998.

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15

Dimov, Ivan T. Monte Carlo methods for applied scientists. World Scientific, 2008.

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16

Fang, Kʻai-tʻai. Number-theoretic methods in statistics. Chapman & Hall, 1994.

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17

Stochastic dynamics and control. Elsevier, 2006.

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18

K, Pang W., and Hou S. H, eds. Vertical density representation and its applications. World Scientific, 2004.

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19

Fu, Michael. Conditional Monte Carlo: Gradient estimation and optimization applications. Kluwer Academic Publishers, 1997.

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20

Zhao, Zhong. Sensitivity of propensity score methods to the specifications. IZA, 2005.

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21

Racine, J. S. The semiparametric approach to the estimation of systems of equations models in the presence of heteroskedasticity of unknown form. Dept. of Economics, York University, 1989.

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22

Inoue, Atsushi. A portmanteau test for serially correlated errors in fixed effects models. National Bureau of Economic Research, 2005.

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23

Feiveson, Alan H. Improving the chi-squared approximation for bivariate normal tolerance regions. NASA, 1993.

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24

Anderson, Gordon. Nonparametric tests for common but unspecified population distributions: A Monte Carlo comparison. Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1994.

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25

Fan, Xitao. SAS® for Monte Carlo studies: A guide for quantitative researchers. SAS Institute, 2002.

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26

Racine, J. S. Semiparamteric estimation in the presence of heteroskedasticity of unknown form. Dept. of Economics, York University, 1989.

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27

Górski, Jarosław. Non-linear models of structures with random geometric and material imperfactions [sic] simulation-based approach. Wydawn. Politechniki Gdańskiej, 2006.

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28

Wetherhold, Robert C. Probability techniques for reliability analysis of composite materials. National Aeronautics and Space Administration, 1994.

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29

R, Nelson Charles. Predictable stock returns: Reality or statistical illusion? National Bureau of Economic Research, 1990.

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30

Richardson, Matthew. Drawing inferences from statistics based on multi-year asset returns. National Bureau of Economic Research, 1990.

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31

Brown, Andrew M. Probabilistic component mode synthesis of nondeterministic substructures. [National Aeronautics and Space Administration, 1997.

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32

Bhatti, M. Ishaq. Non-Nested Regression Models: UK ed. edition. Nova Science Publishers Inc, 2013.

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33

Acta Numerica 1998. Cambridge University Press, 1999.

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34

Lemieux, Christiane. Monte Carlo and Quasi-Monte Carlo Sampling. Springer, 2010.

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35

Plaskota, Leszek, and Henryk Woźniakowski. Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer, 2012.

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36

Lopes, Hedibert, and Nicholas Polson. Analysis of economic data with multiscale spatio-temporal models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.12.

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This article discusses the use of Bayesian multiscale spatio-temporal models for the analysis of economic data. It demonstrates the utility of a general modelling approach for multiscale analysis of spatio-temporal processes with areal data observations in an economic study of agricultural production in the Brazilian state of Espìrito Santo during the period 1990–2005. The article first describes multiscale factorizations for spatial processes before presenting an exploratory multiscale data analysis and explaining the motivation for multiscale spatio-temporal models. It then examines the temp

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37

Allen, Michael P., and Dominic J. Tildesley. Advanced Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0009.

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This chapter describes the ways in which the Monte Carlo importance sampling method may be adapted to improve the calculation of ensemble averages, particularly those associated with free energy differences. These approaches include umbrella sampling, non-Boltzmann sampling, the Wang–Landau method, and nested sampling. In addition, a range of special techniques have been developed to accelerate the simulation of flexible molecules, such as polymers. These approaches are illustrated with scientific examples and program code. The chapter also explains the analysis of such simulations using techn

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38

Raydugin, Yuri G. Modern Risk Quantification in Complex Projects. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198844334.001.0001.

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There are multiple complaints that existing project risk quantification methods—both parametric and Monte Carlo—fail to produce accurate project duration and cost-risk contingencies in a majority of cases. It is shown that major components of project risk exposure—non-linear risk interactions—pertaining to complex projects are not taken into account. It is argued that a project system consists of two interacting subsystems: a project structure subsystem (PSS) and a project delivery subsystem (PDS). Any misalignments or imbalances between these two subsystems (PSS–PDS mismatches) are associated

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39

Monte Carlo Methods for Applied Scientists. World Scientific Publishing Company, 2005.

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40

1955-, Rubino Gerardo, and Tuffin Bruno, eds. Rare event simulation using Monte Carlo methods. Wiley, 2009.

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41

Summary report for the Workshop on Monte Carlo Analysis: U.S. Environmental Protection Agency, New York, NY, May 14-16, 1996. Risk Assessment Forum, The Agency, 1996.

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42

United States. Environmental Protection Agency. Risk Assessment Forum. and Workshop on Monte Carlo Analysis (1996 : New York, N.Y.), eds. Summary report for the Workshop on Monte Carlo Analysis: U.S. Environmental Protection Agency, New York, NY, May 14-16, 1996. Risk Assessment Forum, The Agency, 1996.

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43

Martin, Andrew D. Bayesian Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0021.

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This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods. This is followed by a practical justification for the use of Bayesian methods in the social sciences, and a number of examples from the literature where Bayesian methods have proven useful are shown. The article finally provides a review of modern software for Bayesian infer

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44

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Improving the chi-squared approximation for bivariate normal tolerance regions. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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45

Improving the chi-squared approximation for bivariate normal tolerance regions. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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46

United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Improving the chi-squared approximation for bivariate normal tolerance regions. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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47

Building Algorithmic Trading Systems Website A Traders Journey From Data Mining To Monte Carlo Simulation To Live Trading. John Wiley & Sons Inc, 2014.

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48

Korn, Granino A. Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies. Wiley & Sons, Incorporated, John, 2013.

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49

Korn, Granino A. Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies. Wiley & Sons, Incorporated, John, 2013.

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50

Korn, Granino A. Advanced Dynamic-System Simulation: Model Replication and Monte Carlo Studies. Wiley, 2013.

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