Statistical Methods for Environmental Epidemiology with R A Case Stu
Advances in statistical methodology and computing have played an important role in allowing researchers to more accurately assess the health effects of ambient air pollution. The methods and software developed in this area are applicable to a wide array o
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Roger D.Peng Francesca Dominici
Statistical Methods for Environmental Epidemiology with R A Case Study in Air Pollution and Health
Use R! Series Editors: Robert Gentleman
Kurt Hornik
Giovanni Parmigiani
Use R! Albert: Bayesian Computation with R Bivand/Pebesma/Gomez-Rubio: Applied Spatial Data Analysis with R ´ Claude:Morphometrics with R Cook/Swayne: Interactive and Dynamic Graphics for Data Analysis: With R and GGobi Hahne/Huber/Gentleman/Falcon: Bioconductor Case Studies Nason: Wavelet Methods in Statistics with R Paradis: Analysis of Phylogenetics and Evolution with R Peng/Dominici: Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health Pfaff: Analysis of Integrated and Cointegrated Time Series with R, 2nd edition Sarkar: Lattice: Multivariate Data Visualization with R Spector: Data Manipulation with R
Roger D. Peng
· Francesca Dominici
Statistical Methods for Environmental Epidemiology with R A Case Study in Air Pollution and Health
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Roger D. Peng Francesca Dominici Johns Hopkins Bloomberg School of Public Health 615 N. Wolfe St. Johns Hopkins University Baltimore MD 21205-2179 USA [email protected] [email protected] Series Editors: Robert Gentleman Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Avenue, N. M2-B876 Seattle, Washington 98109 USA
Kurt Hornik Department of Statistik and Mathematik Wirtschaftsuniversität Wien Augasse 2-6 A-1090 Wien Austria
Giovanni Parmigiani The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University 550 North Broadway Baltimore, MD 21205-2011 USA
Library of Congress Control Number: 2008928295
ISBN 978-0-387-78166-2 e-ISBN 978-0-387-78167-9 DOI: 10.1007/978-0-387-78167-9 © 2008 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper. 9 8 7 6 5 4 3 2 1 springer.com
Preface
As an area of statistical application, environmental epidemiology and more specifically, the estimation of health risk associated with the exposure to environmental agents, has led to the development of several statistical methods and software that can then be applied to other scientific areas. The statistical analyses aimed at addressing questions in environmental epidemiology have the following characteristics. Often the signal-to-noi
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