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Libros sobre el tema "Nonlinear regression analysi"

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Publicado: 14 de marzo de 2023

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1

1952-, Wild C. J., ed. Nonlinear regression. New York: Wiley, 1989.

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2

Seber, G. A. F. Nonlinear regression. Hoboken, N.J: Wiley-Interscience, 2003.

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3

Ivanov, A. V. Asymptotic theory of nonlinear regression. Dordrecht: Kluwer Academic Publishers, 1997.

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4

Bates, Douglas M. y Donald G. Watts, eds. Nonlinear Regression Analysis and Its Applications. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1988. http://dx.doi.org/10.1002/9780470316757.

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5

G, Watts Donald, ed. Nonlinear regression analysis and its applications. New York: Wiley, 1988.

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6

Borowiak, Dale S. Model discrimination for nonlinear regression models. New York: M. Dekker, 1989.

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7

Handbook of nonlinear regression models. New York: M. Dekker, 1990.

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8

Pázman, Andrej. Nonlinear statistical models. Dordrecht: Kluwer Academic Publishers, 1993.

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9

Asymptotic Theory of Nonlinear Regression. Dordrecht: Springer Netherlands, 1997.

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10

Nonlinear statistical models. New York: Wiley, 1987.

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11

T, Denison David G., ed. Bayesian methods for nonlinear classification and regression. Chichester, England: Wiley, 2002.

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12

Carroll, Raymond J. Measurement error in nonlinear models. Boca Raton: Chapman & Hall/CRC, 1998.

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13

Antoniadis, Anestis. Régression non linéaire et applications. Paris: Economica, 1992.

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14

Lewis, Peter A. W. Nonlinear modeling of time series using Multivariate Adaptive Regression Splines (MARS). Monterey, Calif: Naval Postgraduate School, 1990.

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15

Linear and nonlinear models: Fixed effects, random effects, and mixed models. Berlin: Walter de Gruyter, 2006.

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16

Directorate, Canada Inland Waters. Modelling the dissolved oxygen change streams using nonlinear regression analysis. Burlington, Ont: Environment Canada, 1987.

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17

Canada. Environment Canada. Inland Waters Directorate. Modelling the Dissolved Oxygen Change in Streams Using Nonlinear Regression Analysis. S.l: s.n, 1988.

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18

El-Shaarawi, A. H. Modelling the dissolved oxygen change in streams using nonlinear regression analysis. Burlington, Ont: Inland Waters Directorate, National Water Research Institute, Canada Centre for Inland Waters, 1988.

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19

Viole, Fred. Nonlinear Nonparametric Statistics: Using Partial Moments. USA: CreateSpace Independent Publishing Platform, 2013.

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20

Statistical tools for nonlinear regression: A practical guide with S-PLUS and R examples. 2a ed. New York: Springer, 2004.

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21

J, Carroll Raymond y Carroll Raymond J, eds. Measurement error in nonlinear models: A modern perspective. 2a ed. Boca Raton, FL: Chapman & Hall/CRC, 2006.

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22

Bunke, Helga y Olaf Bunke, eds. Statistical Methods of Model Building: V2. Nonlinear Regression, Functional Relations and Robust Methods. Chichester, New York, USA: John Wiley & Sons Ltd, 1989.

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23

S, Huet, ed. Statistical tools for nonlinear regression: A practical guide with S-PLUS examples. New York: Springer, 1996.

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24

Altissimo, Filippo. Nonlinear VAR: Some theory and an application to US GNP and unemployment. Rome: Banca d'Italia, 1998.

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25

Altissimo, Filippo. Nonlinear VAR: Some theory and an application to US GNP and unemployment. [Roma]: Banca d'Italia, 1998.

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26

Geological Survey (U.S.), ed. Regression modeling of ground-water flow: Modifications to the computer code for nonlinear regression solution of steady-state ground-water flow problems. [Washington, D.C.?]: U.S. G.P.O., 1993.

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27

Geological Survey (U.S.), ed. Regression modeling of ground-water flow: Modifications to the computer code for nonlinear regression solution of steady-state ground-water flow problems. [Washington, D.C.?]: U.S. G.P.O., 1993.

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28

Geological Survey (U.S.), ed. Regression modeling of ground-water flow: Modifications to the computer code for nonlinear regression solution of steady-state ground-water flow problems. [Washington, D.C.?]: U.S. G.P.O., 1993.

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29

L, Koul H., ed. Weighted empirical processes in dynamic nonlinear models. 2a ed. New York: Springer, 2002.

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30

Rusling, James F. Nonlinear computer modeling of chemical and biochemical data. San Diego: Academic Press, 1996.

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31

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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32

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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33

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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34

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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35

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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36

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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37

Tiedeman, Claire R. Application of nonlinear-regression methods to a ground-water flow model of the Albuquerque Basin, New Mexico. Menlo Park, Calif: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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38

Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson y Viktor Polyak. Econometric modeling in the GRETL package. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1732940.

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Resumen
The tutorial describes the capabilities of the GRETL statistical package for computer data analysis and econometric modeling based on spatial data and time series. Using concrete economic examples, GRETL considers classical and generalized models of linear and nonlinear regression, methods for detecting and eliminating multicollinearity, models with variable structure, autoregressive processes, methods for testing and eliminating autocorrelation, as well as discrete choice models and systems of simultaneous equations. For the convenience of users, the tutorial contains all the task data files used in the work in the format .GDTs are collected in an application in the cloud so that users have access to them. Meets the requirements of the federal state educational standards of higher education of the latest generation in the disciplines of "Econometrics" and "Econometric modeling". For students and teachers of economic universities in the field of Economics, as well as researchers who use econometric methods to model socio-economic phenomena and processes.
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39

Grafarend, Erik. Linear and Nonlinear Models: Fixed effects, random effects, and total least squares. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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40

Wild, C. J. y George A. F. Seber. Nonlinear Regression. Wiley & Sons, Incorporated, John, 2005.

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41

Wild, C. J. y George A. F. Seber. Nonlinear Regression. Wiley & Sons, Incorporated, John, 2005.

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42

Seber, George A. y C. J. Wild. Nonlinear Regression. Wiley & Sons, Incorporated, John, 2005.

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43

Gelman, Andrew y Deborah Nolan. Multiple regression and nonlinear models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0010.

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This chapter covers multiple regression and links statistical inference to general topics such as lurking variables that arose earlier. Many examples can be used to illustrate multiple regression, but we have found it useful to come to class prepared with a specific example, with computer output (since our students learn to run the regressions on the computer). We have found it is a good strategy to simply use a regression analysis from some published source (e.g., a social science journal) and go through the model and its interpretation with the class, asking students how the regression results would have to differ in order for the study’s conclusions to change. The chapter includes examples that revisit the simple linear model of height and income, involve the class in models of exam scores, and fit a nonlinear model (for more advanced classes) for golf putting.
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44

Nonlinear Regression With R. Springer, 2008.

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45

Albert, Arthur E. y Gardner Leland A. Jr. Stochastic Approximation and Nonlinear Regression. MIT Press, 2003.

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46

Knafl, George J. y Kai Ding. Adaptive Regression for Modeling Nonlinear Relationships. Springer, 2016.

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47

Knafl, George J. y Kai Ding. Adaptive Regression for Modeling Nonlinear Relationships. Springer, 2018.

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48

Pázman, Andrej. Nonlinear Statistical Models. Springer, 2013.

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49

Pázman, Andrej. Nonlinear Statistical Models. Springer, 2010.

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50

Transforms & nonlinear regressions: User's manual. Chicago, IL: SPSS Inc., 1997.

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