Dynamic Mixed Models For Familial Longitudinal Data

Dynamic Mixed Models for Familial Longitudinal Data (repost)

Dynamic Mixed Models for Familial Longitudinal Data by Brajendra C. Sutradhar
English | ISBN: 1441983414, 1441983422 | edition 2011 | PDF | 512 pages | 4 mb
Dynamic Mixed Models for Familial Longitudinal Data (repost)

Dynamic Mixed Models for Familial Longitudinal Data by Brajendra C. Sutradhar
English | ISBN: 1441983414, 1441983422 | edition 2011 | PDF | 512 pages | 4 mb

This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models.
Linear Mixed Models for Longitudinal Data (Springer Series in Statistics) by Geert Molenberghs

Linear Mixed Models for Longitudinal Data (Springer Series in Statistics) by Geert Molenberghs
English | June 16, 2000 | ISBN: 0387950273 | 579 Pages | PDF | 39 MB

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure.

Models for Discrete Longitudinal Data (repost)  

Posted by fdts at Sept. 14, 2013
Models for Discrete Longitudinal Data (repost)

Models for Discrete Longitudinal Data
by Geert Molenberghs, Geert Verbeke
English | 2005 | ISBN: 0387251448 | 687 pages | PDF | 5.3 MB
Antedependence Models for Longitudinal Data by Vicente A. Núñez-Antón [Repost]

Antedependence Models for Longitudinal Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Vicente A. Núñez-Antón
English | Aug 19, 2009 | ISBN: 1420064266 | 279 Pages | PDF | 2 MB

The First Book Dedicated to This Class of Longitudinal Models. Although antedependence models are particularly useful for modeling longitudinal data that exhibit serial correlation, few books adequately cover these models.
Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences (repost)

Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences by Edward E. Gbur, Walter W. Stroup, Kevin S. McCarter and Susan Durham
English | 2012 | ISBN-10: 0891181822 | 283 pages | PDF | 21,7 MB

Generalized Linear Mixed Models in the Agricultural and Natural ResourcesSciences provides readers with an understanding and appreciation for the design and analysis of mixed models for non-normally distributed data. It is the only publication of its kind directed specifically toward the agricultural and natural resources sciences audience.

Local Models for Spatial Analysis (repost)  

Posted by Book-er at July 1, 2012
Local Models for Spatial Analysis (repost)

Local Models for Spatial Analysis
English | 2006-10-25 | ISBN: 0415316812 | 244 pages | PDF | 8 MB

In both the physical and social sciences, there are now available large spatial data sets with detailed local information. Global models for analyzing these data are not suitable for investigating local variations; consequently, local models are the subject of much recent research. Collecting a variety of models into a single reference, Local Models for Spatial Analysis explains in detail a variety of approaches for analyzing univariate and multivariate spatial data.
Hidden Markov Models for Time Series: An Introduction Using R (Monographs on Statistics and Applied Probability)

Hidden Markov Models for Time Series
Walter Zucchini, Iain L. MacDonald | Chapman & Hall/CRC | 2009-04-28 | ISBN:1584885734 | Pages: 269 | PDF | 5.3MB

This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It presents an accessible overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations. It explores a variety of applications in animal behavior, finance, epidemiology, climatology, and sociology. The authors discuss how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting. They provide all of the data sets analyzed in the text online.

Longitudinal Data Analysis Using Structural Equation Models (Repost)  eBooks & eLearning

Posted by enmoys at June 9, 2016
Longitudinal Data Analysis Using Structural Equation Models (Repost)

Longitudinal Data Analysis Using Structural Equation Models By John J. McArdle, John R. Nesselroade
2014 | 424 Pages | ISBN: 1433817152 | PDF | 5 MB
Joint Models for Longitudinal and Time-to-Event Data: With Applications in R (Repost)

Joint Models for Longitudinal and Time-to-Event Data: With Applications in R By Dimitris Rizopoulos
2012 | 275 Pages | ISBN: 1439872864 | PDF | 3 MB