Book contents
- Frontmatter
- 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
- Plate section
Preface
Published online by Cambridge University Press: 05 November 2012
- Frontmatter
- 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
- Plate section
Summary
Motivation and goals
For many years, astronomers have struggled with the application of sophisticated statistical methodologies to analyze their rich datasets and address complex astrophysical problems. On one hand, at least in the United States, astronomers receive little or no formal training in statistics. The traditional method of education has been informal exposure to a few familiar methods during early research experiences. On the other hand, astronomers correctly perceive that a vast world of applied mathematical and statistical methodologies has emerged in recent decades. But systematic, broad training in modern statistical methods has not been available to most astronomers.
This volume seeks to address this problem at three levels. First, we present fundamental principles and results of broad fields of statistics applicable to astronomical research. The material is roughly at a level of advanced undergraduate courses in statistics. We also outline some recent advanced techniques that may be useful for astronomical research to give a flavor of the breadth of modern methodology. It is important to recognize that we give only incomplete introductions to the fields, and we guide the astronomer towards more complete and authoritative treatments.
Second, we present tutorials on the application of both simple and more advanced methods applied to contemporary astronomical research datasets using the R statistical software package. R has emerged in recent years as the most versatile public-domain statistical software environment for researchers in many fields.
- Type
- Chapter
- Information
- Modern Statistical Methods for AstronomyWith R Applications, pp. xv - xviiiPublisher: Cambridge University PressPrint publication year: 2012