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Statistical Methods for Astronomical Data Analysis (Hardcover, 2014 ed.)
Loot Price: R3,665
Discovery Miles 36 650
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Statistical Methods for Astronomical Data Analysis (Hardcover, 2014 ed.)
Series: Springer Series in Astrostatistics, 3
Expected to ship within 12 - 17 working days
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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.
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