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TitleModelling Longitudinal and Spatially Correlated Data [electronic resource] / edited by Timothy G. Gregoire, David R. Brillinger, Peter J. Diggle, Estelle Russek-Cohen, William G. Warren, Russell D. Wolfinger
ImprintNew York, NY : Springer New York : Imprint: Springer, 1997
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Descript X, 402 p. online resource


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 Direcยญ 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: โ{128}ข Generalized Linear Models: Peter McCullagh-"Residual Likelihood in Linear and Generalized Linear Models" โ{128}ข Longitudinal Data Analysis: Nan Laird-"Using the General Linear Mixed Model to Analyze Unbalanced Repeated Measures and Longiยญ tudinal Data" โ{128}ข Spatio---Temporal Processes: David R. Brillinger-"Statistical Analyยญ sis of the Tracks of Moving Particles" โ{128}ข Spatial Data Analysis: Noel A. Cressie-"Statistical Models for Latยญ tice Data" โ{128}ข Modelling Messy Data: Raymond J. Carroll-"Some Results on Genยญ eralized Linear Mixed Models with Measurement Error in Covariates" โ{128}ข Future Directions: Peter J


Generalized Linear Models -- Linear Models, Vector Spaces, and Residual Likelihood -- An Assessment of Approximate Maximum Likelihood Estimators in Generalized Linear Models -- Scaled Link Functions for Heterogeneous Ordinal Response Data -- Longitudinal Data Analysis -- Software Design for Longitudinal Data Analysis -- Asymptotic Properties of Nonlinear Mizedโ{128}{148}Effects Models -- Structured Antedependence Models for Longitudinal Data -- Effect of Confounding and Other Misspecification in Models for Longitudinal Data -- The Linear Mixed Model. A Critical Investigation in the Context of Longitudinal Data -- Modelling the Order of Disability Events in Activities of Daily Living Using Discrete Longitudinal Data -- Estimation of Subject Means in Fixed and Mixed Models with Application to Longitudinal Data -- Modeling Toxicological Multivariate Mortality Data: a Bayesian Perspective -- Comparison of Methods for General Nonlinear Mixed-Effects Models -- Repeated Measures Analysis Using Mixed Models: Some Simulation Results -- Spatial Data Analysis -- Object Identification Using Markov Random Field Segmentation Models at Multiple Resolutions of a Rectangular Lattice -- Comparison of Some Sampling Designs for Spatially Clustered Populations -- Using Geostatistical Techniques to Map the Distribution of Tree Species from Ground Inventory Data -- Global Analysis of Ozone Data Based on Spherical Splines -- Bounded Influence Estimation in a Spatial Linear Mixed Model -- Spatial Correlation Models as Applied to Evolutionary Biology -- Rainfall Modelling Using a Latent Guassian Variable -- Estimation of Individual Exposure Following a Chemical Spill in Superior, Wisconsin -- Flexible Response Surface Methods via Spatial Regression and EBLUPS -- Robust Semivariogram Estimation in the Presence of Influential Spatial Data Values -- Modelling Spatio-Temporal Processes -- Elephant Seal Movements: Dive Types and Their Sequences -- Models for Continuous Stationary Space-time Processes -- A Comparison of Two Spatio-temporal Semivariograms with Use in Agriculture -- Structuring Correlation Within Hierarchical Spatio-temporal Models for Disease Rates -- Modelling Messy Data -- Generalized Linear Mixed Measurement Error Models: -- Calculating the Appropriate Information Matrix for Log-linear Models When Data Are Missing at Random -- Nonparametric Regression in the Presence of Correlated Errors -- Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions -- Modelling Correlations Between Diagnostic Tests in Efficacy Studies With an Imperfect Reference Test -- Special Topics and Future Directions -- Combining Standard Block Analyses with Spatial Analyses Under a Random Effects Model -- Spatial and Longitudinal Data Analysis: Two Histories with a Common Future?

Mathematics Mathematical models Statistics Mathematics Mathematical Modeling and Industrial Mathematics Statistics for Life Sciences Medicine Health Sciences


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