Walter W. Piegorsch – författare

This book offers a set of case studies exemplifying the broad range of statistical science used in evnironmental studies and application. The case studies can be used for graduate courses in environmental statistics, as a resource for courses in statistics using genuine examples to illustrate statistical methodology and theory, and for courses in environmental science. Not only are these studies valuable for teaching about an essential cross-disciplinary activity, but also they can be used to spur new research along directions exposed in these examples. The studies reported here resulted from a program of research carried on by the National Institute of Statistical Sciences (NISS) during the years 1992-1996. NISS was created in 1991 as an initiative of the national statistics organizations, with the mission to renew and focus efforts of statistical science on important cross-disciplinary problems. One of NISS' first projects was a cooperative research effort with the U.S. EPA on problems of great interest to environmental science and regulation, surely one of today's most important cross-disciplinary activities.Douglas Nychka is Professor of Statistics at North Carolina State University and currently the Directory of the Geophysical Statistics Project at the National Center for Atmospheric Research. He has made contributions to the modeling of ozone pollution and the design of monitoring networks and has been author on more than 40 scientific papers. Walter W. Piegorsch is Professor of Statistics at the University of South Carolina and studies problems in design and analysis of environmental toxicity studies, low-dose risk extrapolation, and combining environmental information.

Environmental statistics is a rapidly growing field, supported by advances in digital computing power, automated data collection systems, and interactive, linkable Internet software. Concerns over public and ecological health and the continuing need to support environmental policy-making and regulation have driven a concurrent explosion in environmental data analysis. This textbook is designed to address the need for trained professionals in this area. The book is based on a course which the authors have taught for many years, and prepares students for careers in environmental analysis centered on statistics and allied quantitative methods of data evaluation. The text extends beyond the introductory level, allowing students and environmental science practitioners to develop the expertise to design and perform sophisticated environmental data analyses. In particular, it: Provides a coherent introduction to intermediate and advanced methods for modeling and analyzing environmental data. Takes a data-oriented approach to describing the various methods. Illustrates the methods with real-world examples Features extensive exercises, enabling use as a course text. Includes examples of SAS computer code for implementation of the statistical methods. Connects to a Web site featuring solutions to exercises, extra computer code, and additional material. Serves as an overview of methods for analyzing environmental data, enabling use as a reference text for environmental science professionals. Graduate students of statistics studying environmental data analysis will find this invaluable as will practicing data analysts and environmental scientists including specialists in atmospheric science, biology and biomedicine, chemistry, ecology, environmental health, geography, and geology.

This is an expanded and revised edition of an essential reference work for university libraries, research laboratories, government institutions and consultancies concerned with the environmental sciences. The Encyclopedia offers comprehensive coverage of the wide range of topics, techniques and applications that comprise this multidisciplinary field.Key features of the new edition: Expanded to six volumes to reflect developments in Environmetrics techniques and applications during past decadeOver 60% of articles in this edition are new or revisedA new section: Environmental Engineering and TechnologyIncreased breadth and depth of coverage in key application areas such as climate science and hydrological processesRevised and updated treatment of methodology including spatial-temporal statistics, computational statistics and modeling extremesGreater emphasis on practitioner oriented content.Publishing in both print and online the Encyclopedia will be an invaluable aid to researchers, lecturers, students, consultants and policymakers engaged in ecological statistics, climate change, geostatistics, natural resources, risk assessment, environmental health, physical processes, chemometrics, stochastic modelling, forestry, acid rain, and pollution.

Hurricane Katrina slammed into the Gulf Coast in August 2005 with devastating consequences. Almost all analyses of the disaster have been dedicated to the way the hurricane affected New Orleans. This volume examines the impact of Katrina on southern Mississippi. While communities along Mississippi's Gulf Coast shared the impact, their socioeconomic and demographic compositions varied widely, leading to different types and rates of recovery. This volume furthers our understanding of the pace of recovery and its geographic extent, and explores the role of inequalities in the recovery process and those antecedent conditions that could give rise to a 'recovery divide'. It will be especially appealing to researchers and advanced students of natural disasters and policy makers dealing with disaster consequences and recovery.

Hurricane Katrina slammed into the Gulf Coast in August 2005 with devastating consequences. Almost all analyses of the disaster have been dedicated to the way the hurricane affected New Orleans. This volume examines the impact of Katrina on southern Mississippi. While communities along Mississippi's Gulf Coast shared the impact, their socioeconomic and demographic compositions varied widely, leading to different types and rates of recovery. This volume furthers our understanding of the pace of recovery and its geographic extent, and explores the role of inequalities in the recovery process and those antecedent conditions that could give rise to a 'recovery divide'. It will be especially appealing to researchers and advanced students of natural disasters and policy makers dealing with disaster consequences and recovery.

Statistical Data Analytics Statistical Data Analytics Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced. Extensive examples using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others. Statistical Data Analytics: Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques.Provides informative, technical details for the highlighted methods.Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book.Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas.This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.

Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery  A comprehensive introduction to statistical methods for data mining and knowledge discovery.Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.