About statistical modeling
- intro courses:
- "OpenIntro Statistics" by Diez, Barr and Cetinkaya-Rundel (free textbook)
- "Statistics Done Wrong" by Alex Reinhart (free textbook)
- "Mixed effects models for the population approach" by Marc Lavielle and the POPIX team at INRIA (free wiki)
- "Graphical Models" by Zoubin Ghahramani (2012, free video & slides)
- swirl and mosaic, R packages to learn stats and R simultaneously and interactively
- advanced courses:
- "Advanced Data Analysis from an Elementary Point of View" by Cosma Shalizi (free book)
- "A First Course in Bayesian Statistical Methods" by Peter Hoff (2010, book)
- "Bayesian Data Analysis" by Andrew Gelman & co (2013, free slides, 3rd edition of the book)
- "Statistical Decision Theory and Bayesian Analysis" by James Berger (1993, 2nd edition of the book)
- "Intermediate Statistics" by Larry Wasserman (free lecture notes)
- "Stat Fact Sheets" by Eric Anderson (free tex files)
- "MCMC for Stat Gen" by Eric Anderson and Matthew Stephens (free lecture notes)
- mathematical aspects:
- "Introduction to Linear Algebra" by Gilbert Strang (free videos, book)
- "Matrix Differential Calculus with Applications in Statistics and Econometrics" by Magnus and Neudecker (2007, free pdf for the 3rd edition)
- practical, computational aspects:
- "How to share data with a statistician" by Jeff Leek (procedure on GitHub), see also the advice on genomics metadata by Raphael Irrizary and "statistical consulting" by Karl Broman (slides)
- "Exploratory Data Analysis with R" by Jennifer Bryan (free course)
- "Tutorial on Big Data with Python" by Marcel Caraciolo (free Python notebooks)
- interpreted languages: obviously R, but more and more Python (SciPy for NumPy, Matplotlib, and pandas, but see also scikit-learn and statsmodels), as well as others (Julia)
- C/C++: GSL, Armadillo, Eigen, Rcpp, Stan
- editor: obviously Emacs (language-agnostic, org-mode, etc), but also Rstudio (R-only...) and IPython (Python-only...)
- visualizing, plotting:
- "Visualizing uncertainty about the future" by Spiegelhalter et al. (Science 2011, DOI)
- "Let's practice what we preach: turning tables into graphs" by Gelman et al. (The American Statistician 2002, DOI)
- "Top ten worst graphs" by Karl Broman (webpage)
- "EDA: Investigate, Visualize, and Summarize Data Using Ra" (on Udacity, free courseware available)
- philosophy, history, pragmatism:
- "Statistical analysis and the illusion of objectivity" by Berger and Berry (American Scientist 1988, DOI, pdf)
- "Bayesian methods: general background" by E. T. Jaynes (1985, free pdf) and "Where do we stand on maximum entropy?" by E. T. Jaynes (1978, free pdf)
- "The Philosophy of Statistics" by Lindley (JRSSD 2000, DOI)
- "What is statistics?" by Feinberg (An.Rev.Stat.Appl. 2014, DOI)
- "Mathematical Models and Reality: A Constructivist Perspective" by Christian Hennig (Foundations of Science 2010, DOI)
- "Philosophy and the practice of Bayesian statistics" by Andrew Gelman and Cosma Shalizi (British Journal of Mathematical and Statistical Psychology 2013, DOI)
- "Statistical Inference : the Big Picture" by Robert Kass (Statistical Science 2011, DOI, free pdf on arXiv)
- "In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist" by Roderick Little (JASA 2013, DOI)
- "Des spécificités de l’approche bayésienne et de ses justifications en statistique inférentielle" par Christian Robert (chapitre 2013, pdf gratuit sur HAL)
- classics:
- list from Christian Robert
- litterature, community:
- Annals of Statistics, JRSSB, JASA, Annals of Applied Statistics, Bayesian Analysis, JMRL, NIPS
- Biometrics, Biostatistics, Biometrika
- Statistical Science, The American Statistician, Annual Review of Statistics and its Application
- see also on Project Euclid and arXiv
- blogs: Andrew Gelman, Christian Robert, Larry Wasserman
- links with society: JRSSA, Statistique et Société (free pdfs)
- personal experiences: Petr Keil
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