Tutorials and Schools

• R Tutorials (Penn State - IIA Summer School 2008)
• Summer School 2007,   R tutorials
• Markov chain Monte Carlo   Examples using R


• Tutorials and Opening Workshop for Astrostatistics Program at SAMSI, January 2006
• Summer School 2006
• R tutorials, 2006   Exercises in R, an open-source environment for statistical computing and graphics


• Summer School 2005

Astronomical datasets for statistical analysis

• Univariate datasets
• Distance to the LMC (N=1, repeated measurements, measurement errors)
• Asteroid densities (N=28, density estimation, measurement errors)
• Globular cluster luminosity function (N~81+360, normal model, truncation, 2-sample test)
• Planetary nebula luminosity function (N~238+45+101+59, k-sample test, nonparametrics, semi-parametrics, truncation)

• Censored datasets
• Planet host stars (N=39 and 29; survival analysis, Kaplan-Meier, 2-sample, linear regression, censoring)
  

• Multivariate datasets
  
• Shapley galaxy redshift catalog (N=4,315, p=5, spatial point process, hierarchical clustering, wavelets, measurement errors)
• Hipparcos star catalog (N=2,719, p=8, multivariate clustering, mixture models, regression, measurement errors, outliers)
  
• COMBO-17 galaxy photometric catalog (N=3,462, p=65, multivariate analysis, regression, clustering, truncation)
  
• SDSS quasar catalog (N=46,420, p=23, multivariate analysis, regression, clustering, measurement errors, truncation, )
  
• New SDSS quasar catalog (N=77,429, p=18, multivariate analysis, regression, clustering, measurement errors, truncation, )
  

• Signal processing (spectra and time series)
  
• Chandra Orion star flares (N=209, 678 and 14,258, time series analysis, inhomogeneous Poisson process, Bayesian modeling, wavelets)
• Extrasolar planet radial velocities (N=17, 52, 138, periodicity detection, nonlinear regression, parameter estimation, measurement error, Bayesian modeling)
  
• Galaxy spectra (feature characterization, parameter estimation)
• Quasar absorption spectra (feature characterization, parameter estimation, normal mixture model)

• Model selection
  
  • Gamma-ray burst afterglow modelling (N=63, p=2, linear regression, breakpoints, measurement errors, model elaboration and parsimony)

• Images
• Chandra star cluster (Poisson signal detection, Poisson image deconvolution, parameter estimation)