Modern Statistical Methods for Astronomy: With R ApplicationsModern 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. |
Contents
1 | 6 |
Probability | 13 |
Statistical inference | 35 |
Probability distribution functions | 76 |
Nonparametric statistics | 105 |
density estimation | 128 |
Regression | 150 |
Multivariateanalysis | 190 |
1 | 261 |
Time series analysis | 292 |
Spatialpointprocesses | 337 |
AppendixA Notationandacronyms | 379 |
AppendixC Astronomicaldatasets | 399 |
| 434 | |
| 462 | |
| 470 | |
Other editions - View all
Modern Statistical Methods for Astronomy: With R Applications Eric D. Feigelson,G. Jogesh Babu Limited preview - 2012 |
Common terms and phrases
algorithm applied asteroids astronomical astronomical dataset astrophysical asymptotic autocorrelation autoregressive bandwidth Bayesian inference binomial bivariate bootstrap calculated censoring Chapter classification coefficients components computed confidence intervals consider correlation covariance CRAN package cross-validation data points distance distribution function Equation evenly spaced example Figure galaxies Gaussian gives globular cluster heteroscedastic histogram hypothesis tests independent kernel density estimator least-squares linear regression luminosity function magnitudes mathematical matrix maximum likelihood estimation measurement errors median methods mixture models model selection multivariate datasets noise nonlinear nonparametric normal distribution objects observed outliers parameters Pareto periodogram plot Poisson distribution population power-law probability problems procedures properties provides quantile quasars random variables redshift regression model relationship resampling residuals robust SDSS Section Shapley Supercluster simulations smoothing spatial point processes spectral stars statistical stellar stochastic structure techniques training set truncated univariate values variance variogram vector weighted X-ray