Climate Time Series Analysis PDF Download
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Author: Manfred Mudelsee
Publisher: Springer Science & Business Media
ISBN: 9048194822
Category : Science
Languages : en
Pages : 497
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Book Description
Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.
Author: Manfred Mudelsee
Publisher: Springer Science & Business Media
ISBN: 9048194822
Category : Science
Languages : en
Pages : 497
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Book Description
Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.
Author: Donald B. Percival
Publisher: Cambridge University Press
ISBN: 9780521685085
Category : Computers
Languages : en
Pages : 628
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Book Description
This introduction to wavelet analysis 'from the ground level and up', and to wavelet-based statistical analysis of time series focuses on practical discrete time techniques, with detailed descriptions of the theory and algorithms needed to understand and implement the discrete wavelet transforms. Numerous examples illustrate the techniques on actual time series. The many embedded exercises - with complete solutions provided in the Appendix - allow readers to use the book for self-guided study. Additional exercises can be used in a classroom setting. A Web site offers access to the time series and wavelets used in the book, as well as information on accessing software in S-Plus and other languages. Students and researchers wishing to use wavelet methods to analyze time series will find this book essential.
Author: T. Subba Rao
Publisher: Chapman and Hall/CRC
ISBN:
Category : Mathematics
Languages : en
Pages : 502
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Book Description
Very Good,No Highlights or Markup,all pages are intact.
Author: Gutti Jogesh Babu
Publisher: CRC Press
ISBN: 9780412983917
Category : Mathematics
Languages : en
Pages : 242
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Book Description
Modern astronomers encounter a vast range of challenging statistical problems, yet few are familiar with the wealth of techniques developed by statisticians. Conversely, few statisticians deal with the compelling problems confronted in astronomy. Astrostatistics bridges this gap. Authored by a statistician-astronomer team, it provides professionals and advanced students in both fields with exposure to issues of mutual interest. In the first half of the book the authors introduce statisticians to stellar, galactic, and cosmological astronomy and discuss the complex character of astronomical data. For astronomers, they introduce the statistical principles of nonparametrics, multivariate analysis, time series analysis, density estimation, and resampling methods. The second half of the book is organized by statistical topic. Each chapter contains examples of problems encountered astronomical research and highlights methodological issues. The final chapter explores some controversial issues in astronomy that have a strong statistical component. The authors provide an extensive bibliography and references to software for implementing statistical methods. The "marriage" of astronomy and statistics is a natural one and benefits both disciplines. Astronomers need the tools and methods of statistics to interpret the vast amount of data they generate, and the issues related to astronomical data pose intriguing challenges for statisticians. Astrostatistics paves the way to improved statistical analysis of astronomical data and provides a common ground for future collaboration between the two fields.
Author: Rifaat Abdalla
Publisher: BoD – Books on Demand
ISBN: 1803563052
Category : Computers
Languages : en
Pages : 206
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Book Description
Time series data consist of a collection of observations obtained through repeated measurements over time. When the points are plotted on a graph, one of the axes is always time. Time series analysis is a specific way of analyzing a sequence of data points. Time series data are everywhere since time is a constituent of everything that is observable. As our world becomes increasingly digitized, sensors and systems are constantly emitting a relentless stream of time series data, which has numerous applications across various industries. The editors of this book are happy to provide the specialized reader community with this book as a modest contribution to this rapidly developing domain.
Author: Rajmund Przybylak
Publisher: Springer Science & Business Media
ISBN: 0306482223
Category : Science
Languages : en
Pages : 338
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Book Description
It has been known at least since the end of the century that the polar areas play a very important role in the formation of the Earth’s climates. It is also known today that they are the most sensitive regions to climatic change, and are thus perfect case studies for the detection of such changes. The most serious obstacle to the study of climatic and other geographical elements of the polar areas (including the Arctic) has always been the severe climatic conditions which prevail in these regions. Because of these extreme con- tions, research into particular elements of the climatic system (including the atmosphere) began here much later than it did in lower latitudes. For instance, the whole area of the Arctic was not sufficiently covered with a network of meteorological stations until the late 1940s (and even then there were large areas of the central Arctic and the Greenland interior for which no data were available). This is probably why it was not until the start of the 1990s that a body of work began to appear which analysed in any depth climatic variability for the Arctic as a whole. While a considerable number of papers had been p- lished before this period, most of them were local studies presenting highly localised information, providing air temperature measurements but often little else.
Author: Jean-Luc Starck
Publisher: Cambridge University Press
ISBN: 0521599148
Category : Image processing
Languages : en
Pages : 301
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Book Description
Powerful techniques have been developed in recent years for the analysis of digital data, especially the manipulation of images. This book provides an in-depth introduction to a range of these innovative, avante-garde data-processing techniques. It develops the reader's understanding of each technique and then shows with practical examples how they can be applied to improve the skills of graduate students and researchers in astronomy, electrical engineering, physics, geophysics and medical imaging. What sets this book apart from others on the subject is the complementary blend of theory and practical application. Throughout, it is copiously illustrated with real-world examples from astronomy, electrical engineering, remote sensing and medicine. It also shows how many, more traditional, methods can be enhanced by incorporating the new wavelet and multiscale methods into the processing. For graduate students and researchers already experienced in image processing and data analysis, this book provides an indispensable guide to a wide range of exciting and original data-analysis techniques.
Author: Brian J. McLean
Publisher: Springer Science & Business Media
ISBN: 9400914857
Category : Science
Languages : en
Pages : 508
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Book Description
Large area sky surveys are now a reality in the radio, IR, optical and X-ray passbands. In the next few years, new surveys using optical, UV and IR mosaic cameras with high throughput digital detectors will expand the dynamic range and accuracy of photometry and astrometry of objects over a significant fraction of the entire sky. Parallel X-ray and radio surveys over the same areas will produce astronomical image and spectroscopic databases of unprecedented size and quality. The combined data sets will provide significant new constraints on star formation, stellar dynamics, Galactic structure, the evolution of galaxies and large scale structure, as well as new opportunities to identify rare objects in the solar system and the Galaxy. Large area surveys have formidable data acquisition, processing, archiving, and data distribution demands and this meeting provided a forum for sharing experiences amongst workers specializing in different wavebands as well as discussing how multiband observations can reveal fundamental relationships in our understanding of the Universe.
Author: Alexander von Eye
Publisher: Elsevier
ISBN: 0080550827
Category : Social Science
Languages : en
Pages : 403
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Book Description
Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text. - Presents accessible methods of regression analysis - Includes a broad spectrum of methods - Techniques are explained step-by-step - Provides sample command and result files for SYSTAT
Author: Aileen Nielsen
Publisher: O'Reilly Media
ISBN: 1492041629
Category : Computers
Languages : en
Pages : 500
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Book Description
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
