Modern Statistical Methods for Astronomy: With R Applications
Cambridge University Press, 7/12/2012
EAN 9780521767279, ISBN10: 052176727X
Hardcover, 490 pages, 25.4 x 19.7 x 2.5 cm
Language: English
Originally published in English
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.
Advance praise: 'Feigelson and Babu, two of the leading figures in the new discipline of astrostatistics, have written a text that surely must be considered as the standard text on the subject. The book presents astronomers with an up-to-date overview of the foremost methods being used in astrostatistical analysis, providing numerous examples, as well as relevant R code, for how these methods can be used in their research. The text is useful to astronomers who are new to serious astrostatistical analysis, as well as to seasoned researchers.' Joseph M. Hilbe, Chair, ISI International Astrostatistics Network, Arizona State University/Jet Propulsion Laboratory