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Könyv Modelling Longitudinal and Spatially Correlated Data Timothy G. Gregoire

Modelling Longitudinal and Spatially Correlated Data

Szerző: Timothy G. Gregoire
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Springer-Verlag New York Inc.
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
39 076 Ft
Correlated data arise in numerous contexts across a wide spectrum of subject-matter disciplines. Mod...

Információk a könyvről

Szerző
Timothy G. Gregoire
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
1997
oldal
402
EAN
9780387982168
ISBN
0387982167
Enbook ID
04067064
Kiadó
Springer-Verlag New York Inc.
Súly
1290
Méretek
155 x 235 x 23

Teljes leírás

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.

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