Synopses & Reviews
This volume focuses on the statistical treatment of continuous and discrete data measured at different points in time, locations in space, and/or across combined spatio-temporal dimensions. Linear, nonlinear, and generalized linear models and methods are presented, as are new developments to handle messy data. The volume provides an examination of the historical development of approaches to model spatially and temporally correlated data and the ongoing convergence of these methods. The papers are based on ones presented at a conference in Nantucket, Massachusetts in October 1996.
Synopsis
Correlated 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.
Synopsis
This refereed volume includes papers presented at a conference on modeling longitudinal and spatially correlated data. Many of the best researchers in the world presented papers in an area with important applications to biostatistics and the environmental sciences.
Table of Contents
Contents: Generalized Linear Models: Linear Models, Vector Spaces,and Residual Likeihood 1, Peter McCullagh: An Assessment of Approximate Maximum Likelihood Estimators in Generalized Linear Models 11, John M.Neuhaus and Mark R.Segal: Scaled Link Functions for Heterogeneous Ordinal Response Data 23, Minge Xie, Douglas G.Simpson,and Raymond J.Carroll: Longitudinal Data Analysis: Software Design for Longitudinal Data Analysis 37, Douglas M.Bates and Jos C.Pinheiro: Asymptotic Properties of Nonlinear Mixed- Effects Models 49, Eugene Demidenko: Structured Antedependence Models for Longitudinal Data 63, Dale L.Zimmerman and Vicente N $ez-Ant n: Effect of Confounding and Other Misspecification in Models for Longitudinal Data 77, Mari Palta, Chin-Yu Lin,and Wei-Hsiung Chao: The Linear Mixed Model. A Critical Investigation in the Context of Longitudinal Data 89, Geert Verbeke and Emmanuel Lasaffre: Modelling the Order of Disability Events in Activities of Daily Living Using Discrete Longitudinal Data 101, Dorothy D.Dunlop and Larry M.Manheim: Estimation of Subject Means in Fixed and Mixed Models with Application to Longitudinal Data 111, Edward J.Stanek III: Modeling Toxicological