Modern Statistical Methods for Astronomy
With R Applications

A unique resource introducing graduate students and researchers in astronomy to advanced statistics through ready-to-use code.

Eric D. Feigelson (Author), G. Jogesh Babu (Author)

9780521767279, Cambridge University Press

Hardback, published 12 July 2012

490 pages, 100 b/w illus. 12 colour illus. 30 tables 59 exercises
25.3 x 19.5 x 2.5 cm, 1.24 kg

'… statistics textbooks for astronomy are surprisingly rare, so this book represents a welcome addition to the literature … the text is written clearly and is easy to understand … an excellent text. Graduate students would especially benefit from this book … but seasoned researchers are likely to discover new methods for their research as well.' Jason C. Speights, Journal of the American Statistical Association

Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book can be found at www.cambridge.org/msma. Material available on their website includes datasets, R code and errata.

1. Introduction
2. Probability
3. Statistical inference
4. Probability distribution functions
5. Nonparametric statistics
6. Density estimation or data smoothing
7. Regression
8. Multivariate analysis
9. Clustering, classification and data mining
10. Nondetections: censored and truncated data
11. Time series analysis
12. Spatial point processes
Appendices
Index.

Subject Areas: Astronomy, space & time [PG], Mathematics & science [P]