Modelling Longitudinal and Spatially Correlated Data

1. Front Matter

   Pages i-x

   PDF

2. Generalized Linear Models

   1. Linear Models, Vector Spaces, and Residual Likelihood

      • Peter McCullagh

      Pages 1-10

   2. An Assessment of Approximate Maximum Likelihood Estimators in Generalized Linear Mixed Models

      • John M. Neuhaus, Mark R. Segal

      Pages 11-22

   3. Scaled Link Functions for Heterogeneous Ordinal Response Data*

      • Minge Xie, Douglas G. Simpson, Raymond J. Carroll

      Pages 23-36

3. Longitudinal Data Analysis

   1. Software Design for Longitudinal Data Analysis

      • Douglas M. Bates, José C. Pinheiro

      Pages 37-48

   2. Asymptotic Properties of Nonlinear Mixed-Effects Models

      • Eugene Demidenko

      Pages 49-62

   3. Structured Antedependence Models for Longitudinal Data

      • Dale L. Zimmerman, Vicente Núñez-Antón

      Pages 63-76

   4. Effect of Confounding and Other Misspecification in Models for Longitudinal Data

      • Mari Palta, Chin-Yu Lin, Wei-Hsiung Chao

      Pages 77-87

   5. The Linear Mixed Model. A Critical Investigation in the Context of Longitudinal Data

      • Geert Verbeke, Emmanuel Lesaffre

      Pages 89-99

   6. Modeling the Order of Disability Events in Activities of Daily Living Using Discrete Longitudinal Data

      • Dorothy D. Dunlop, Larry M. Manheim

      Pages 101-110

   7. Estimation of Subject Means in Fixed and Mixed Models with Application to Longitudinal Data

      • Edward J. Stanek III

      Pages 111-122

   8. Modeling Toxicological Multivariate Mortality Data: a Bayesian Perspective

      • Debajyoti Sinha, Dipak K. Dey, Hui-May Chu

      Pages 123-133

   9. Comparison of Methods for General Nonlinear Mixed-Effects Models

      • Thérèse A. Stukel, Eugene Demidenko

      Pages 135-146

   10. Repeated Measures Analysis Using Mixed Models: Some Simulation Results

       • S. Paul Wright, Russell D. Wolfinger

       Pages 147-157

4. Spatial Data Analysis

   1. Object Identification Using Markov Random Field Segmentation Models at Multiple Resolutions of a Rectangular Lattice

      • Jeffrey D. Helterbrand, Noel Cressie

      Pages 159-173

   2. Comparison of Some Sampling Designs for Spatially Clustered Populations

      • Mary C. Christman

      Pages 175-185

   3. Using Geostatistical Techniques to Map The Distribution of Tree Species From Ground Inventory Data

      • Rachel Riemann Hershey, Martin A. Ramirez, David A. Drake

      Pages 187-198

   4. Global Analysis of Ozone Data Based on Spherical Splines

      • Meng Jie, Dominique Haughton, Nicholas Teebagy

      Pages 199-209

   5. Bounded Influence Estimation in a Spatial Linear Mixed Model

      • Ana F. Militino, M. Dolores Ugarte

      Pages 211-220

   6. Spatial Correlation Models as Applied to Evolutionary Biology

      • Mary C. Christman, Robert W. Jernigan

      Pages 221-232

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.

• Estimator
• Generalized linear model
• Measure
• Time series
• data analysis

• Department of Forestry, Virginia Polytechnic University and State Institute, Blacksburg, USA

  Timothy G. Gregoire

• Department of Statistics, University of California, Berkeley, Berkeley, USA

  David R. Brillinger

• Department of Mathematics and Statistics, University of Lancaster, Lancaster, England

  Peter J. Diggle

• Department of Animal Sciences, University of Maryland, College Park, College Park, USA

  Estelle Russek-Cohen

• Department of Fisheries and Oceans, St. John’s, Canada

  William G. Warren

• SAS Campus Drive, SAS Institute Inc., Cary, USA

  Russell D. Wolfinger

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