The Analysis of Correlation in Longitudinal and Spatial Data
Author: Christl Ann Donnelly
Publisher:
Published: 1992
Total Pages: 208
ISBN-13:
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Modelling Longitudinal and Spatially Correlated Data
Author: Timothy G. Gregoire
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 404
ISBN-13: 1461206995
DOWNLOAD EBOOKCorrelated data arise in numerous contexts across a wide spectrum of subject-matter disciplines. Modeling such data present special challenges and opportunities that have received increasing scrutiny by the statistical community in recent years. In October 1996 a group of 210 statisticians and other scientists assembled on the small island of Nantucket, U. S. A. , to present and discuss new developments relating to Modelling Longitudinal and Spatially Correlated Data: Methods, Applications, and Future Direc tions. Its purpose was to provide a cross-disciplinary forum to explore the commonalities and meaningful differences in the source and treatment of such data. This volume is a compilation of some of the important invited and volunteered presentations made during that conference. The three days and evenings of oral and displayed presentations were arranged into six broad thematic areas. The session themes, the invited speakers and the topics they addressed were as follows: • Generalized Linear Models: Peter McCullagh-"Residual Likelihood in Linear and Generalized Linear Models" • Longitudinal Data Analysis: Nan Laird-"Using the General Linear Mixed Model to Analyze Unbalanced Repeated Measures and Longi tudinal Data" • Spatio---Temporal Processes: David R. Brillinger-"Statistical Analy sis of the Tracks of Moving Particles" • Spatial Data Analysis: Noel A. Cressie-"Statistical Models for Lat tice Data" • Modelling Messy Data: Raymond J. Carroll-"Some Results on Gen eralized Linear Mixed Models with Measurement Error in Covariates" • Future Directions: Peter J.
Analysis of Longitudinal Data with Example
Author: You-Gan Wang
Publisher: CRC Press
Published: 2022-01-28
Total Pages: 248
ISBN-13: 1498764622
DOWNLOAD EBOOKDevelopment in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas. Key Features: •Includes the most up-to-date methods •Use simple examples to demonstrate complex methods •Uses real data from a number of areas •Examples utilize R code
Longitudinal Data Analysis
Author: Garrett Fitzmaurice
Publisher: CRC Press
Published: 2008-08-11
Total Pages: 633
ISBN-13: 142001157X
DOWNLOAD EBOOKAlthough many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory
Analysis of Longitudinal Data
Author: Peter Diggle
Publisher: Oxford University Press, USA
Published: 2013-03-14
Total Pages: 397
ISBN-13: 0199676755
DOWNLOAD EBOOKThis second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
Longitudinal Data with Serial Correlation
Author: Richard .H. Jones
Publisher: CRC Press
Published: 2018-05-04
Total Pages: 250
ISBN-13: 1351434624
DOWNLOAD EBOOKThis monograph is written for students at the graduate level in biostatistics, statistics or other disciplines that collect longitudinal data. It concentrates on the state space approach that provides a convenient way to compute likelihoods using the Kalman filter.
A Primer in Longitudinal Data Analysis
Author: Toon W Taris
Publisher: SAGE
Published: 2000-05-25
Total Pages: 175
ISBN-13: 1412933587
DOWNLOAD EBOOK`The author has done a remarkable job of writing a very accessible introduction to a broad literature. As such, he should be congratulated on achieving his objective to provide the "ideal primer for this growing area of social research"′ - Kwantitatieve Methoden This accessible introduction to the theory and practice of longitudinal research takes the reader through the strengths and weaknesses of this kind of research, making clear: how to design a longitudinal study; how to collect data most effectively; how to make the best use of statistical techniques; and how to interpret results. Although the book provides a broad overview of the field, the focus is always on the practical issues arising out of longitudinal research. This book supplies the student with all that they need to get started and acts as a manual for dealing with opportunities and pitfalls. It is the ideal primer for this growing area of social research.
Analysis of Longitudinal and Cluster-correlated Data
Author: Nan M. Laird
Publisher: IMS
Published: 2004
Total Pages: 168
ISBN-13: 9780940600607
DOWNLOAD EBOOKMethods and Applications of Longitudinal Data Analysis
Author: Xian Liu
Publisher: Elsevier
Published: 2015-09-01
Total Pages: 531
ISBN-13: 0128014822
DOWNLOAD EBOOKMethods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: - descriptive methods for delineating trends over time - linear mixed regression models with both fixed and random effects - covariance pattern models on correlated errors - generalized estimating equations - nonlinear regression models for categorical repeated measurements - techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. - From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis - Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection - Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.
Analysis of Longitudinal Data with Example
Author: You-Gan Wang
Publisher: CRC Press
Published: 2022-01-28
Total Pages: 213
ISBN-13: 1351649671
DOWNLOAD EBOOKDevelopment in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas. Key Features: •Includes the most up-to-date methods •Use simple examples to demonstrate complex methods •Uses real data from a number of areas •Examples utilize R code
