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Modelling Longitudinal and Spatially Correlated Data

  • Conference proceedings
  • © 1997

Overview

Editors:
  1. Timothy G. Gregoire

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

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  2. David R. Brillinger

    1. Department of Statistics, University of California, Berkeley, Berkeley, USA

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  3. Peter J. Diggle

    1. Department of Mathematics and Statistics, University of Lancaster, Lancaster, England

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  4. Estelle Russek-Cohen

    1. Department of Animal Sciences, University of Maryland, College Park, College Park, USA

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  5. William G. Warren

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

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  6. Russell D. Wolfinger

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

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Part of the book series: Lecture Notes in Statistics (LNS, volume 122)

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Table of contents (34 papers)

  1. Front Matter

    Pages i-x
    Download chapter 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, JosĂ© 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-AntĂłn
      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

      • ThĂ©rèse 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
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Keywords

About this book

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.

Editors and Affiliations

  • 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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