Generalized Estimating Equations

Generalized Estimating Equations, Second Edition  eBooks & eLearning

Posted by roxul at May 12, 2016
Generalized Estimating Equations, Second Edition

James W. Hardin, "Generalized Estimating Equations, Second Edition"
English | ISBN: 1439881138 | 2013 | 277 pages | PDF | 2 MB

Generalized Estimating Equations (Repost)  

Posted by Specialselection at Jan. 31, 2014
Generalized Estimating Equations (Repost)

James W. Hardin, Joseph M. Hilbe, "Generalized Estimating Equations"
English | 2002-07-30 | ISBN: 1584883073 | 224 pages | PDF | 2.8 mb
Generalized Sylvester Equations: Unified Parametric Solutions

Generalized Sylvester Equations: Unified Parametric Solutions by Guang-Ren Duan
English | 20 July 2015 | ISBN: 1482243962 | 466 Pages | AZW4/PDF (True) | 17.08 MB

Provides One Unified Formula That Gives Solutions to Several Types of GSEs. Generalized Sylvester equations (GSEs) are applied in many fields, including applied mathematics, systems and control, and signal processing. Generalized Sylvester Equations: Unified Parametric Solutions presents a unified parametric approach for solving various types of GSEs.
Numerical Methods for Nonlinear Estimating Equations (Oxford Statistical Science, Vol. 29) (Repost)

Numerical Methods for Nonlinear Estimating Equations (Oxford Statistical Science, Vol. 29) by Christopher G. Small
English | 2003 | ISBN: 0198506880 | 322 Pages | PDF | 4 MB

Non linearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis.
Numerical Methods for Nonlinear Estimating Equations (repost)

Numerical Methods for Nonlinear Estimating Equations (Oxford Statistical Science Series) by Christopher G. Small
English | December 11, 2003 | ISBN: 0198506880 | Pages: 328 | PDF | 4 MB

Non linearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis. More entrenched forms of nonlinearity often require intensive numerical methods to construct estimators, and the use of root search algorithms, or one-step estimators, is a standard method of solution.
Correlated Data Analysis: Modeling, Analytics, and Applications by Peter X. -K. Song [Repost]

Correlated Data Analysis: Modeling, Analytics, and Applications (Springer Series in Statistics) by Peter X. -K. Song
Springer; 2007 edition | July 27, 2007 | English | ISBN: 0387713921 | 353 pages | PDF | 3 MB

This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas.
Correlated Data Analysis: Modeling, Analytics, and Applications  (repost)

Correlated Data Analysis: Modeling, Analytics, and Applications
Publisher: Springer | 2007-07-20 | 352 pages | ISBN: 0387713921 | PDF | 3,5 MB

This book presents some recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to handle a broader range of data types than those analyzed by traditional generalized linear models. One example is correlated angular data.
This book provides a systematic treatment for the topic of estimating functions. Under this framework, both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to marginal models and mixed-effects models, this book covers topics on joint regression analysis based on Gaussian copulas and generalized state space models for longitudinal data from long time series.
Correlated Data Analysis: Modeling, Analytics, and Applications (Springer Series in Statistics)

Correlated Data Analysis: Modeling, Analytics, and Applications (Springer Series in Statistics)
Springer | ISBN 0387713921 | 2007-07-20 | PDF |352 pages | 3.31 MB

This book presents some recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to handle a broader range of data types than those analyzed by traditional generalized linear models. One example is correlated angular data. This book provides a systematic treatment for the topic of estimating functions. Under this framework, both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to marginal models and mixed-effects models, this book covers topics on joint regression analysis based on Gaussian copulas and generalized state space models for longitudinal data from long time series.
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide by Jos W. R. Twisk

Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide by Jos W. R. Twisk
Publisher: Cambridge University Press; 1 edition (April 28, 2003) | ISBN: 0521819768 | Pages: 318 | PDF | 3.59 MB

The most important techniques available for longitudinal data analysis are discussed in this book. The discussion includes simple techniques such as the paired t-test and summary statistics, but also more sophisticated techniques such as generalized estimating equations and random coefficient analysis. A distinction is made between longitudinal analysis with continuous, dichotomous, and categorical outcome variables.
Generalized Difference Methods for Differential Equations by Zhongying Chen [Repost]

Generalized Difference Methods for Differential Equations: Numerical Analysis of Finite Volume Methods by Zhongying Chen
English | Jan 3, 2000 | ISBN: 0824703308 | 459 Pages | PDF | 15 MB

This text presents a comprehensive mathematical theory for elliptic, parabolic, and hyperbolic differential equations. It compares finite element and finite difference methods and illustrates applications of generalized difference methods to elastic bodies, electromagnetic fields, underground water pollution, and coupled sound-heat flows.