User:Timothee Flutre/Notebook/Postdoc/2011/11/16

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(About statistical modeling: add blog post R. Irrizary)
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** "Mixed effects models for the population approach" by Marc Lavielle and the POPIX team at INRIA (free [http://popix.lixoft.net/index.php?title=Home_page wiki])
** "Mixed effects models for the population approach" by Marc Lavielle and the POPIX team at INRIA (free [http://popix.lixoft.net/index.php?title=Home_page wiki])
** "Graphical Models" by Zoubin Ghahramani (2012, free [http://videolectures.net/mlss2012_ghahramani_graphical_models/ video & slides])
** "Graphical Models" by Zoubin Ghahramani (2012, free [http://videolectures.net/mlss2012_ghahramani_graphical_models/ video & slides])
 +
** [http://swirlstats.com/ swirl], R package to learn stats and R simultaneously and interactively
* '''advanced courses''':
* '''advanced courses''':

Revision as of 14:10, 6 February 2014

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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, R package 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)
  • 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)
  • philosophy, history, pragmatism:
    • "Statistical analysis and the illusion of objectivity" by Berger and Berry (American Scientist 1988, DOI, pdf)
    • "Where do we stand on maximum entropy?" by E. T. Jaynes (1978, free pdf)
    • "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


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