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Tuesday, August 18, 2020 | History

9 edition of Statistical Methods for Spatio-Temporal Systems (Monographs on Statistics and Applied Probability) found in the catalog.

Statistical Methods for Spatio-Temporal Systems (Monographs on Statistics and Applied Probability)

  • 250 Want to read
  • 19 Currently reading

Published by Chapman & Hall/CRC .
Written in English

    Subjects:
  • Probability & statistics,
  • Mathematics,
  • Science/Mathematics,
  • Probability & Statistics - General,
  • Mathematics / Statistics,
  • Biostatistics,
  • Space and time,
  • Spatial analysis (Statistics),
  • Statistical methods,
  • Time-series analysis

  • Edition Notes

    ContributionsBarbel Finkenstadt (Editor), Leonhard Held (Editor), Valerie Isham (Editor)
    The Physical Object
    FormatHardcover
    Number of Pages286
    ID Numbers
    Open LibraryOL8795492M
    ISBN 101584885939
    ISBN 109781584885931

    Statistics for Spatio-Temporal Data (Wiley Desktop Editions) A state-of-the-art presentation of spatio-temporal processes,bridging classic ideas with modern hierarchical statisticalmodeling concepts and the latest computational methods From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species. Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest developments and methodologies for analyzing and understanding extreme environmental and hydrological events. The book addresses the topic using spatio-temporal methods, such as space-time geostatistics, machine learning, statistical theory, hydrological modelling.

    Winner of the DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winners of the PROSE Award in the Mathematics category, for the book “Statistics for Spatio-Temporal Data” (), published by /5(12). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice.

    Spatio-temporal Statistical Methods in Environmental and Biometrical Problems Edited by Jorge Mateu, Rosa M. Crujeiras, Raquel Menezes, Francisco Montes Vol Part 2. Find many great new & used options and get the best deals for Chapman and Hall/CRC Texts in Statistical Science Ser.: Spatio-Temporal Methods in Environmental Epidemiology by James V. Zidek and Gavin Shaddick (Trade Cloth) at the best online prices at eBay! Free shipping for many products!


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Statistical Methods for Spatio-Temporal Systems (Monographs on Statistics and Applied Probability) Download PDF EPUB FB2

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling : Hardcover.

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling cturer: Chapman and Hall/CRC.

Book Description. Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities.

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling by: Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling buted by.

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. Statistical Methods for Spatio-Temporal Systems.

Statistical Methods for Spatio-Temporal Systems book. Edited By Barbel Finkenstadt, Leonhard Held, Valerie Isham. Edition 1st Edition. First Published eBook Published 20 October one often aims to develop a : Barbel Finkenstadt, Leonhard Held, Valerie Isham.

"Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities."--Jacket.

Request PDF | Statistical Methods for Spatio-Temporal Systems | Preface Spatio-Temporal Point Processes: Methods and Applications Peter J. Diggle Spatio-Temporal Modeling-With a. Buy Statistical Methods for Spatio-temporal Systems: (Monographs on Statistics and Applied Probability) (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) 1 by Barbel Finkenstadt, Leonhard Held, Valerie Isham (ISBN: ) from Amazon's Book Store.

Everyday low prices and free delivery on eligible orders. Winner of the DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods Noel Cressie and Christopher K.

Wikle, are also winners of the PROSE Award in the Mathematics category, for the book “Statistics for Spatio-Temporal Data” ().

this kind a spatio-temporal point pattern, and the underlying stochastic model for the data a spatio-temporal point process. Motivating examples Amacrine cells in the retina of a rabbit One general approach to analysing spatio-temporal point process data is to extend existing methods for purely spatial data by considering the time of.

This website,is meant to serve several purposes: It is a landing page for our book Spatio-Temporal Statistics with R (including an associated R package STRbook, reviews, and an errata sheet); it offers an opportunity for individuals to participate in a Discussion Forum; and it is a place where new software, data sets, and articles on spatio-temporal statistics can.

Presents statistical research issues on spatio-temporal data modeling that promotes advances in research and an understanding between the mechanistic and the statistical modeling communities.

This book offers a variety of statistical methods, including likelihood-based, nonparametric smoothing, Fourier, wavelet, and Markov chain Monte Carlo.

Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest developments and methodologies for analyzing and understanding extreme environmental and hydrological events.

The book addresses the topic using spatio-temporal methods, such as space-time geostatistics, machine. Many of these systems use statistical algorithms to look for anomalies and to trigger epidemiologic investigation, quantification, localization and outbreak management. This book discusses the design and evaluation of statistical methods for effective biosurveillance for readers with minimal statistical Cited by: Winner of the DeGroot Prize.

A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winners of the PROSE Award in the Mathematics category, for the book "Statistics for Spatio-Temporal Data"/5.

In recent years, with rapid industrialization and massive energy consumption, ground-level ozone (O 3) has become one of the most severe air pollutants.

In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal.

“It is a wonderful place to begin studying spatio-temporal processes.” (Mathematical Reviews Clippings, 1 January )“Overall, I believe this academic monograph would be an excellent reference book for researchers and graduate students who are interested in a systematic and indepth understanding of statistical approaches to spatio-temporal data analysis and modeling.” (Journal of the.

As the number, volume and resolution of spatio-temporal datasets increases, traditional statistical methods for dealing with such data are becoming overwhelmed. Nevertheless, the spatio-temporal data are rich sources of information and knowledge, waiting to be discovered.

This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April If information on the spatio-temporal origin of data are available, this information can be exploited for statistical modeling in various ways; this is the study of spatio-temporal statistics [see.

Cressie’s () classic book (Statistics for Spatial Data) has some sparse coverage of spatio-temporal statistics. From that, it is pretty clear that formal consideration of this topic started in the mids, largely tied to the increased interest in environmental issues (e.g., air pollution) at the time.