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Modelling Longitudinal and Spatially Correlated Data

  • Timothy G. Gregoire
  • David R. Brillinger
  • Peter J. Diggle
  • Estelle Russek-Cohen
  • William G. Warren
  • Russell D. Wolfinger

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

Table of contents

  1. Front Matter
    Pages i-x
  2. Generalized Linear Models

  3. Longitudinal Data Analysis

    1. Douglas M. Bates, José C. Pinheiro
      Pages 37-48
    2. Dale L. Zimmerman, Vicente Núñez-Antón
      Pages 63-76
    3. Mari Palta, Chin-Yu Lin, Wei-Hsiung Chao
      Pages 77-87
    4. Debajyoti Sinha, Dipak K. Dey, Hui-May Chu
      Pages 123-133
    5. Thérèse A. Stukel, Eugene Demidenko
      Pages 135-146
    6. S. Paul Wright, Russell D. Wolfinger
      Pages 147-157
  4. Spatial Data Analysis

    1. Rachel Riemann Hershey, Martin A. Ramirez, David A. Drake
      Pages 187-198
    2. Meng Jie, Dominique Haughton, Nicholas Teebagy
      Pages 199-209
    3. Ana F. Militino, M. Dolores Ugarte
      Pages 211-220
    4. Mary C. Christman, Robert W. Jernigan
      Pages 221-232
    5. C. A. Glasbey, I. M. Nevison
      Pages 233-242
    6. Elena N. Naumova, Timothy C. Haas, Robert D. Morris
      Pages 243-254
    7. M. O’Connell, R. Wolfinger
      Pages 255-264
  5. Modelling Spatio-Temporal Processes

    1. David R. Brillinger, Brent S. Stewart
      Pages 275-288
    2. Richard H. Jones, Yiming Zhang
      Pages 289-298
    3. Lance A. Waller, Bradley P. Carlin, Hong Xia
      Pages 309-319
  6. Modelling Messy Data

  7. Special Topics and Future Directions

About these proceedings

Introduction

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.

Keywords

Estimator Generalized linear model Measure Time series data analysis

Editors and affiliations

  • Timothy G. Gregoire
    • 1
  • David R. Brillinger
    • 2
  • Peter J. Diggle
    • 3
  • Estelle Russek-Cohen
    • 4
  • William G. Warren
    • 5
  • Russell D. Wolfinger
    • 6
  1. 1.Department of ForestryVirginia Polytechnic University and State InstituteBlacksburgUSA
  2. 2.Department of StatisticsUniversity of California, BerkeleyBerkeleyUSA
  3. 3.Department of Mathematics and StatisticsUniversity of LancasterLancasterEngland
  4. 4.Department of Animal SciencesUniversity of Maryland, College ParkCollege ParkUSA
  5. 5.Department of Fisheries and OceansSt. John’sCanada
  6. 6.SAS Campus DriveSAS Institute Inc.CaryUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-0699-6
  • Copyright Information Springer-Verlag New York, Inc. 1997
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-98216-8
  • Online ISBN 978-1-4612-0699-6
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site