
Modern Statistical Methods for Astronomy
With R Applications
- English
- PDF
- Available on iOS & Android
About this book
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.
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Information
Table of contents
- Cover
- Modern Statistical Methods for Astronomy
- Title
- Copyright
- Dedication
- Contents
- Preface
- 1: Introduction
- 2: Probability
- 3: Statistical inference
- 4: Probability distribution functions
- 5: Nonparametric statistics
- 6: Data smoothing: density estimation
- 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
- Appendix A: Notation and acronyms
- Appendix B: Getting started with R
- Appendix C: Astronomical datasets
- References
- Subject index
- R and CRAN commands