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008			150903s2008 xxu| o |||| 0|eng d
020			_a9780387781679 _99780387781679
024	7		_a10.1007/9780387781679 _2doi
035			_avtls000332897
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100	1		_aDominici, Francesca. _eautor _9303085
245	1	0	_aStatistical Methods for Environmental Epidemiology with R : _bA Case Study in Air Pollution and Health / _cby Francesca Dominici, Roger D. Peng.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_ax, 144 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aUse R
500			_aSpringer eBooks
505	0		_aStudies of Air Pollution and Health -- to R and Air Pollution and Health Data -- Reproducible Research Tools -- Statistical Issues in Estimating the Health Effects of Spatial–Temporal Environmental Exposures. -- Exploratory Data Analyses -- Statistical Models -- Pooling Risks Across Locations and Quantifying Spatial Heterogeneity -- A Reproducible Seasonal Analysis of Particulate Matter and Mortality in the United States.
520			_aAdvances 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 of problems in environmental epidemiology. This book provides an overview of the methods used for investigating the health effects of air pollution and gives examples and case studies in R which demonstrate the application of those methods to real data. The book will be useful to statisticians, epidemiologists, and graduate students working in the area of air pollution and health and others analyzing similar data. The authors describe the different existing approaches to statistical modeling and cover basic aspects of analyzing and understanding air pollution and health data. The case studies in each chapter demonstrate how to use R to apply and interpret different statistical models and to explore the effects of potential confounding factors. A working knowledge of R and regression modeling is assumed. In-depth knowledge of R programming is not required to understand and run the examples. Researchers in this area will find the book useful as a ``live'' reference. Software for all of the analyses in the book is downloadable from the web and is available under a Free Software license. The reader is free to run the examples in the book and modify the code to suit their needs. In addition to providing the software for developing the statistical models, the authors provide the entire database from the National Morbidity Mortality and Air Pollution Study (NMMAPS) in a convenient R package. With the database, readers can run the examples and experiment with their own methods and ideas. Roger D. Peng is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for spatial and temporal data. Dr. Peng is the author of numerous R packages and is a frequent contributor to the R mailing lists. Francesca Dominici is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. She has published extensively on hierarchical and semiparametric modeling and has been the leader of major national studies of the health effects of air pollution. She has also participated in numerous panels conducted by the National Academy of Science assessing the health effects of environmental exposures and has consulted for the US Environmental Protection Agency's Clean Air Act Advisory Board.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aPeng, Roger D. _eautor _9303086
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387781662
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-78167-9 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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