Books on the topic 'Modeling analyses'

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1

Brakman, Steven. International trade modeling: Decomposition analyses. [Groningen, Netherlands: Wolters-Noordhoff, 1991.

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2

Paul, Pukite, ed. Modeling for reliability analysis: Markov modeling for reliability, maintainability, safety, and supportability analyses of complex computer systems. New York: IEEE Press, 1998.

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3

Charisse, Faraut, ed. Mastering portraiture: Advanced analyses of the face sculpted in clay. Honeoye, NY: PCF Studios, Inc., 2009.

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4

Institute Of Electrical and Electronics Engineers. IEEE guide for synchronous generator modeling practices in stability analyses. New York, NY, USA: Institute of Electrical and Electronics Engineers, 1991.

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5

Modeling socioeconomic evolution and continuity in ancient Egypt: The value and limitations of zooarchaeological analyses. Oxford: Archaeopress, 2004.

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6

Hansen, Daniel L. Modeling, simulation, and analyses of attitude control for the Crew/Equipment Retriever (CER) proposed for Space Station. Monterey, Calif: Naval Postgraduate School, 1989.

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7

Modeling social heterogeneity, neighborhoods and local influences on urban real estate prices: Spatial dynamic analyses in the Belo Horizonte metropolitan area, Brazil. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, 2009.

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8

Furtado, Bernardo Alves. Modeling social heterogeneity, neighborhoods and local influences on urban real estate prices: Spatial dynamic analyses in the Belo Horizonte metropolitan area, Brazil. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, 2009.

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9

Close, Charles M. Modeling and analysis of dynamic systems. 3rd ed. New York: Wiley, 2002.

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10

1934-, Frederick Dean K., ed. Modeling and analysis of dynamic systems. 2nd ed. Boston,Mass: Houghton Mifflin, 1993.

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11

1934-, Frederick Dean K., ed. Modeling and analysis of dynamic systems. 2nd ed. New York: Wiley, 1995.

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12

1934-, Frederick Dean K., ed. Modeling and analysis of dynamic systems. 2nd ed. Boston: Houghton Mifflin, 1993.

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13

International Symposium on Flood Frequency and Risk Analyses (1986 Louisiana State University, Baton Rouge). Hydrologic frequency modeling: Proceedings of the International Symposium on Flood Frequency and Risk Analyses, 14-17 May 1986, Louisiana State University, Baton Rouge, U.S.A. Dordrecht: D. Reidel Pub. Co., 1987.

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14

Skousen, Royal. Analogical modeling of language. Dordrecht: Kluwer Academic Publishers, 1989.

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15

Modeling & decision analysis. New York: South-Western Cengage Learning, 2008.

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16

P, Rózsa, ed. Applied dimensional analysis and modeling. New York: McGraw Hill, 1998.

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17

P, Rózsa, ed. Applied dimensional analysis and modeling. 2nd ed. Amsterdam: Elsevier/Butterworth-Heinemann, 2007.

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18

1950-, Yao David D., ed. Stochastic modeling and analysis of manufacturing systems. New York: Springer-Verlag, 1994.

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19

Pardoe, Iain. Applied regression modeling. 2nd ed. Hoboken, NJ: Wiley, 2012.

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20

M, Gupta Surendra, ed. Remanufacturing modeling and analysis. Boca Raton: CRC Press, 2012.

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21

David, Kelton W., ed. Simulation modeling and analysis. 3rd ed. Boston: McGraw-Hill, 2000.

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22

David, Kelton W., ed. Simulation modeling and analysis. New York, N.Y: McGraw-Hill, 1991.

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23

R, Anderson David. Modeling and Decision Analysis. Mason: South-Western College Publishing, 2000.

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24

David, Kelton W., ed. Simulation modeling and analysis. 2nd ed. New york: McGraw-Hill, 1991.

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25

Amyot, Daniel, and Alan W. Williams, eds. System Analysis and Modeling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b105884.

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26

Structural modeling and analysis. Cambridge, U.K: Cambridge University Press, 1997.

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27

1923-, Bell Stoughton, ed. Mathematical analysis for modeling. Boca Raton, Fla: CRC Press, 1999.

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28

Daniel, Otto, Deller Steven C, and Johnson Thomas G, eds. Community policy analysis modeling. Ames, Iowa: Blackwell Pub., 2006.

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29

Nelson, Barry L. Stochastic modeling: Analysis & simulation. Mineloa, N.Y: Dover Publications, 2002.

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30

Symposium, LMS Durham, ed. Numerical analysis of multiscale problems. Heidelberg: Springer, 2012.

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31

Hallett, Andrew Hughes, and Peter McAdam, eds. Analyses in Macroeconomic Modelling. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5219-2.

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32

Andrew, Lawson, and Denison David G. T, eds. Spatial cluster modelling. Boca Raton, FL: Chapman & Hall/CRC, 2002.

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33

Reddy, J. N. Geometrically nonlinear analysis laminated elastic structures. [Washington, DC]: National Aeronautics and Space Administration, 1993.

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34

Workshop on Advanced Methods of Physiological System Modeling (3rd 1988 Marina del Rey, Calif.). Advanced methods of physiological system modeling. New York: Published in cooperation with Biomedical Simulations Resource, University of Southern California, Los Angeles by Plenum, 1989.

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35

Miksza, Peter, and Kenneth Elpus. Structural Equation Modeling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0014.

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This chapter presents structural equation modeling as a tool for conducting research regarding how collections of variables may be related to each other as well as to a particular outcome or even multiple outcomes. Structural equation modeling refers to a collection of analytical techniques that can be used to model complex patterns of predictive relationships among a collection of both measured and latent variables. As a statistical tool, structural equation modeling combines the features of regression and factor analysis. The chapter offers conceptual illustrations and practical steps for carrying out structural equation modeling by describing mediation and moderation analyses in the context of music education research.
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36

Shi, Wenzhong. Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses. Taylor & Francis Group, 2009.

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37

Shi, Wenzhong. Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses. Taylor & Francis Group, 2009.

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38

Shi, Wenzhong. Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses. Taylor & Francis Group, 2020.

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39

Wenzhong, Shi. Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses. Taylor & Francis Group, 2009.

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40

Dogramaci, Ali. Developments in Econometric Analyses of Productivity: Measurement and Modeling Issues. Springer, 2011.

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41

Dogramaci, Ali. Developments in Econometric Analyses of Productivity: Measurement and Modeling Issues. Springer, 2012.

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42

McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.
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43

Institute Of Electrical and Electronics Engineers. IEEE Guide for Synchronous Generator Modeling Practices in Stability Analyses/Sh14332. Inst of Elect & Electronic, 1991.

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44

de Marchi, Scott, and Scott E. Page. Agent‐Based Modeling. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0004.

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This article provides a discussion on agent-based modeling. Two examples that show the ability of computational methods to extend game-theoretic results are presented. It then discusses modeling agents, modeling agent interactions, and system behaviour. In addition, it describes how agent-based models differ from and complement mathematical models and concludes with some suggestions for how one might best leverage the strengths of agent-based models to advance political science. Most mathematical analyses of game-theoretic models do not look into the stability and attainability of their equilibria and would be made richer by complementing them with agent-based models that explored those properties. The ability of computational models to test the robustness of formal results would be reason alone to add them to tool kits. As a methodology, agent-based modeling should be considered as in its infancy, its enormous potential limited only by the scientific and creative talents of its practitioners.
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45

Recker, Jan. Evaluations of Process Modeling Grammars: Ontological, Qualitative and Quantitative Analyses Using the Example of BPMN. Springer, 2011.

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46

Recker, Jan. Evaluations of Process Modeling Grammars: Ontological, Qualitative and Quantitative Analyses Using the Example of BPMN. Springer London, Limited, 2011.

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47

Chambers, Robert G. Competitive Agents in Certain and Uncertain Markets. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190063016.001.0001.

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This book uses concepts from optimization theory to develop an integrated analytic framework for treating consumer, producer, and market equilibrium analyses as special cases of a generic optimization problem. The same framework applies to both stochastic and non-stochastic decision settings, so that the latter is recognized as an (important) special case of the former. The analytic techniques are borrowed from convex analysis and variational analysis. Special emphasis is given to generalized notions of differentiability, conjugacy theory, and Fenchel's Duality Theorem. The book shows how virtually identical conjugate analyses form the basis for modeling economic behavior in each of the areas studied. The basic analytic concepts are borrowed from convex analysis. Special emphasis is given to generalized notions of differentiability, conjugacy theory, and Fenchel's Duality Theorem. It is demonstrated how virtually identical conjugate analyses form the basis for modelling economic behaviour in each of the areas studied.
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48

Shibuya, Ryosuke. Dynamical Characteristics of Inertia-Gravity Waves in the Antarctic Mesosphere: Analyses Combining High-Resolution Observations and Modeling. Springer, 2020.

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49

Shibuya, Ryosuke. Dynamical Characteristics of Inertia-Gravity Waves in the Antarctic Mesosphere: Analyses Combining High-Resolution Observations and Modeling. Springer, 2020.

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50

Shibuya, Ryosuke. Dynamical Characteristics of Inertia-Gravity Waves in the Antarctic Mesosphere: Analyses Combining High-Resolution Observations and Modeling. Springer Singapore Pte. Limited, 2021.

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