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Books on the topic 'Models of generalized estimating equations'

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Published: 25 May 2024

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1

Gregory, Allan W. Estimating equations with combined moving average error processes under rational expectations. London, Canada: Dept. of Economics, University of Western Ontario, 1985.

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2

author, Ieno Elena N., ed. Beginner's guide to zero-inflated models with R. Newburgh, United Kingdom: Highland Statistics Ltd., 2016.

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3

Ziegler, Andreas. Generalized Estimating Equations. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0499-6.

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4

Joseph, Hilbe, ed. Generalized estimating equations. Boca Raton, FL: Chapman & Hall/CRC, 2003.

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5

service), SpringerLink (Online, ed. Generalized Estimating Equations. New York, NY: Springer Science+Business Media, LLC, 2011.

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6

1944-, Hilbe Joseph M., ed. Quasi-least squares regression. Boca Raton: CRC Press, Taylor & Francis Group, 2014.

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7

Jinfang, Wang, ed. Numerical methods for nonlinear estimating equations. Oxford: Clarendon Press, 2003.

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8

Bayoumi, Tamim A. Estimating trade equations from aggregate bilateral data. [Washington, D.C.]: International Monetary Fund, Asia and Pacific Department, 1999.

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9

Pseudo Maximum Likelihood Methode und Generalised Estimating Equations zur Analyse korrelierter Daten. Frankfurt am Main: P. Lang, 1999.

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10

Knafl, George J. Modeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41988-1.

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11

A, Steeves Peter, Waite Andrew M, Geological Survey (U.S.), and Rhode Island. Water Resources Board, eds. Equations for estimating selected streamflow statistics in Rhode Island. Reston, Virginia: U.S. Geological Survey, 2014.

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12

Lumer, Günter. Evolution equations: Solutions for irregular evolution problems via generalized solutions and generalized initial values: applications to periodic shocks models. Saarbrücken: Universität des Saarlandes, 1994.

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13

Savolainen, Pekka. Modeling of non-isothermal vapor membrane separation with thermodynamic models and generalized mass transfer equations. Lappeenranta, Finland: Lappeenranta University of Technology, 2002.

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14

Straub, T. D. Equations for estimating Clark unit-hydrograph parameters for small rural watersheds in Illinois. Urbana, Ill: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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15

Straub, T. D. Equations for estimating Clark unit-hydrograph parameters for small rural watersheds in Illinois. Urbana, Ill: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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16

Straub, Timothy D. Equations for estimating Clark unit-hydrograph parameters for small rural watersheds in Illinois. Urbana, Ill: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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17

Straub, Timothy D. Equations for estimating Clark unit-hydrograph parameters for small rural watersheds in Illinois. Urbana, Ill: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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18

E, Jennings Marshall, Thomas Wilbert O. 1943-, Riggs H. C, United States. Federal Highway Administration, United States. Federal Emergency Management Agency, and Geological Survey (U.S.), eds. Nationwide summary of U.S. Geological Survey regional regression equations for estimating magnitude and frequency of floods for ungaged sites, 1993. Reston, Va: U.S. Geological Survey, 1994.

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19

Lanning-Rush, Jennifer. Regional equations for estimating mean annual and mean seasonal runoff for natural basins in Texas, base period 1961-90. Austin, Tex: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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20

Lanning-Rush, Jennifer. Regional equations for estimating mean annual and mean seasonal runoff for natural basins in Texas, base period 1961-90. Austin, Tex: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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21

United States. Forest Service. Northern Research Station, ed. Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. Newtown Square, PA: U.S. Dept. of Agriculture, Forest Service, Northern Research Station, 2011.

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22

Novikov, Anatoliy, Tat'yana Solodkaya, Aleksandr Lazerson, and 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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Abstract:
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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23

Generalized Estimating Equations. CRC Press, 2012.

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24

Thyregod, Poul, and Henrik Madsen. Introduction to General and Generalized Linear Models. Taylor & Francis Group, 2010.

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25

Introduction To General And Generalized Linear Models. CRC Press, 2010.

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26

Thyregod, Poul, and Henrik Madsen. Introduction to General and Generalized Linear Models. Taylor & Francis Group, 2010.

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27

Thyregod, Poul, and Henrik Madsen. Introduction to General and Generalized Linear Models. Taylor & Francis Group, 2010.

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28

Nelder, John A., Yudi Pawitan, and Youngjo Lee. Generalized Linear Models with Random Effects. Taylor & Francis Group, 2021.

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29

Nelder, John A., Yudi Pawitan, and Youngjo Lee. Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood. Taylor & Francis Group, 2006.

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30

Nelder, John A., Yudi Pawitan, and Youngjo Lee. Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood, Second Edition. Taylor & Francis Group, 2017.

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31

Nelder, John A., Yudi Pawitan, and Youngjo Lee. Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood. Taylor & Francis Group, 2010.

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32

Nelder, John A., Yudi Pawitan, and Youngjo Lee. Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood, Second Edition. Taylor & Francis Group, 2018.

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33

Nelder, John A., Yudi Pawitan, and Youngjo Lee. Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood, Second Edition. Taylor & Francis Group, 2018.

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34

Nelder, John A., Yudi Pawitan, and Youngjo Lee. Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood, Second Edition. Taylor & Francis Group, 2018.

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35

Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood, Second Edition. Taylor & Francis Group, 2018.

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36

Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood (Monographs on Statistics and Applied Probability). Chapman & Hall/CRC, 2006.

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37

Hilbe, Joseph M., and James W. Hardin. Generalized Estimating Equations. Taylor & Francis Group, 2003.

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38

Hardin, James W., and Joseph M. Hilbe. Generalized Estimating Equations. Chapman and Hall/CRC, 2012. http://dx.doi.org/10.1201/b13880.

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39

Hardin, James W., and Joseph M. Hilbe. Generalized Estimating Equations. Chapman and Hall/CRC, 2002. http://dx.doi.org/10.1201/9781420035285.

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40

Hilbe, Joseph M., and James W. Hardin. Generalized Estimating Equations. Taylor & Francis Group, 2002.

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41

Hilbe, Joseph M., and James W. Hardin. Generalized Estimating Equations. Taylor & Francis Group, 2012.

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42

Hilbe, Joseph M., and James W. Hardin. Generalized Estimating Equations. Taylor & Francis Group, 2012.

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43

Hilbe, Joseph M., and James W. Hardin. Generalized Estimating Equations. Chapman & Hall/CRC, 2002.

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44

Hilbe, Joseph M., and Justine Shults. Quasi-Least Squares Regression. Taylor & Francis Group, 2014.

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45

Hilbe, Joseph M., and Justine Shults. Quasi-Least Squares Regression. Taylor & Francis Group, 2014.

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46

Hardin, James W. Generalized Estimating Equations, Second Edition. Taylor & Francis Group, 2012.

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47

Small, Christopher G., and Jinfang Wang. Numerical Methods for Nonlinear Estimating Equations. Oxford University Press, Incorporated, 2003.

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48

Ray, Santanu Saha, and Subhadarshan Sahoo. Generalized Fractional Order Differential Equations Arising in Physical Models. Taylor & Francis Group, 2018.

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49

Ray, Santanu Saha, and Subhadarshan Sahoo. Generalized Fractional Order Differential Equations Arising in Physical Models. Taylor & Francis Group, 2018.

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50

Generalized Fractional Order Differential Equations Arising in Physical Models. Taylor & Francis Group, 2018.

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