Logo - springer
Slogan - springer

Statistics - Physical & Information Science | Statistical Methods for Astronomical Data Analysis

Statistical Methods for Astronomical Data Analysis

Chattopadhyay, Asis Kumar, Chattopadhyay, Tanuka

2014, XIII, 349 p. 64 illus., 46 illus. in color.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$79.99

(net) price for USA

ISBN 978-1-4939-1507-1

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$109.00

(net) price for USA

ISBN 978-1-4939-1506-4

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • Provides a concise introduction to statistical astronomy in context of recent discoveries
  • Details various traditional and non-traditional methodologies for analyzing and interpreting large scale data sets in new field
  • Integrates R and provides data sets for readers to learn from

This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for astronomical problems, including regression techniques, along with their usefulness for data set problems related to size and dimension. Analysis of missing data is an important part of the book because of its significance for work with astronomical data. Both existing and new techniques related to dimension reduction and clustering are illustrated through examples. There is detailed coverage of applications useful for classification, discrimination, data mining and time series analysis. Later chapters explain simulation techniques useful for the development of physical models where it is difficult or impossible to collect data. Finally, coverage of the many R programs for techniques discussed makes this book a fantastic practical reference. Readers may apply what they learn directly to their data sets in addition to the data sets included by the authors.

Content Level » Research

Keywords » Astronomy Data & Data Mining - Astrostatistics Data Mining - Astrostatistics Monte Carlo Simulation - Astrostatistics Time Series - Introduction to Astrophysics - Large-Scale Data Sets - R for Astrostatistics & Astronomy - Statistical Astronomy

Related subjects » Astronomy - Astrophysics and Astroparticles - Physical & Information Science - Statistical Theory and Methods

Table of contents 

Introduction to Astrophysics.- Introduction to Statistics.- Sources of Astronomical Data.- Statistical Inference.- Advanced Regression and its Application with Measurement Error.- Missing Observations and Imputation.- Dimension Reduction and Clustering.- Clustering, Classification and Data Mining.- Time Series Analysis.- Monte Carlo Simulation.- Uses of Softwares.- Appendix.  

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.