User:Timothee Flutre/Notebook/Postdoc/2011/11/16: Difference between revisions

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==About statistical modeling==
==About statistical modeling==


* '''great courses''':
* '''courses''':
** "Advanced Data Analysis from an Elementary Point of View" by Cosma Shalizi (free [http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ book])
** "Advanced Data Analysis from an Elementary Point of View" by Cosma Shalizi (free [http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ book])
** "A First Course in Bayesian Statistical Methods" by Peter Hoff ([http://www.amazon.com/gp/product/0387922997 book])
** "A First Course in Bayesian Statistical Methods" by Peter Hoff ([http://www.amazon.com/gp/product/0387922997 book])
** "Bayesian Data Analysis" by Andrew Gelman (free [http://www.stat.columbia.edu/~gelman/book/slides slides], [http://www.amazon.com/dp/1439840954 book])
** "Bayesian Data Analysis" by Andrew Gelman (free [http://www.stat.columbia.edu/~gelman/book/slides slides], [http://www.amazon.com/dp/1439840954 book])
** "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])
** "OpenIntro Statistics" by Diez, Barr and Cetinkaya-Rundel (free [http://www.openintro.org/stat/textbook.php textbook])


* '''mathematical aspects''':
* '''mathematical aspects''':
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* '''practical, computational aspects''':
* '''practical, computational aspects''':
** "How to share data with a statistician" by Jeff Leek (free on [https://github.com/jtleek/datasharing github])
** "How to share data with a statistician" by Jeff Leek (free on [https://github.com/jtleek/datasharing procedure] on GitHub)
** "Exploratory Data Analysis with R" by Jennifer Bryan (free [http://www.stat.ubc.ca/~jenny/STAT545A/2012-lectures/ course])
** "Exploratory Data Analysis with R" by Jennifer Bryan (free [http://www.stat.ubc.ca/~jenny/STAT545A/2012-lectures/ course])
** "Tutorial on Big Data with Python" by Marcel Caraciolo (free Python [https://github.com/marcelcaraciolo/big-data-tutorial notebooks])
** "Tutorial on Big Data with Python" by Marcel Caraciolo (free Python [https://github.com/marcelcaraciolo/big-data-tutorial notebooks])
** interpreted languages: obviously [http://openwetware.org/wiki/User:Timothee_Flutre/Notebook/Postdoc/2011/11/07 R], but more and more Python ([http://www.scipy.org/ SciPy] for NumPy, IPython, Matplotlib, and pandas, but also [http://scikit-learn.org/ scikit-learn] and [http://statsmodels.sourceforge.net/ statsmodels]), as well as others (Julia?)
** interpreted languages: obviously [http://openwetware.org/wiki/User:Timothee_Flutre/Notebook/Postdoc/2011/11/07 R], but more and more Python ([http://www.scipy.org/ SciPy] for NumPy, IPython, Matplotlib, and pandas, but also [http://scikit-learn.org/ scikit-learn] and [http://statsmodels.sourceforge.net/ statsmodels]), as well as others (Julia?)
** C/C++: [http://en.wikipedia.org/wiki/GNU_Scientific_Library GSL], [http://en.wikipedia.org/wiki/Armadillo_%28C++_library%29 Armadillo], [http://en.wikipedia.org/wiki/Eigen_(C%2B%2B_library) Eigen], [http://www.rcpp.org/ Rcpp], [http://mc-stan.org/ Stan]
** C/C++: [http://en.wikipedia.org/wiki/GNU_Scientific_Library GSL], [http://en.wikipedia.org/wiki/Armadillo_%28C++_library%29 Armadillo], [http://en.wikipedia.org/wiki/Eigen_(C%2B%2B_library) Eigen], [http://www.rcpp.org/ Rcpp], [http://mc-stan.org/ Stan]
** editor: [https://openwetware.org/wiki/User:Timothee_Flutre/Notebook/Postdoc/2012/07/25 Emacs]
** editor: obviously [https://openwetware.org/wiki/User:Timothee_Flutre/Notebook/Postdoc/2012/07/25 Emacs] (language-agnostic, org-mode, etc)


* '''visualizing, plotting''':
* '''visualizing, plotting''':
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** "Statistical Inference : the Big Picture" by Robert Kass (Statistical Science 2011, [http://dx.doi.org/10.1214/10-STS337 DOI], free [http://arxiv.org/pdf/1106.2895v2.pdf pdf] on arXiv)
** "Statistical Inference : the Big Picture" by Robert Kass (Statistical Science 2011, [http://dx.doi.org/10.1214/10-STS337 DOI], free [http://arxiv.org/pdf/1106.2895v2.pdf pdf] on arXiv)
** "In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist" by Roderick Little (JASA 2013, [http://dx.doi.org/10.1080/01621459.2013.787932 DOI])
** "In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist" by Roderick Little (JASA 2013, [http://dx.doi.org/10.1080/01621459.2013.787932 DOI])
** "Des spécificités de l’approche bayésienne et de ses justifications en statistique inférentielle" by Christian Robert (chapitre 2013, free [http://hal.archives-ouvertes.fr/docs/00/87/01/24/PDF/Bayes.pdf pdf] on HAL)
** "Des spécificités de l’approche bayésienne et de ses justifications en statistique inférentielle" par Christian Robert (chapitre 2013, [http://hal.archives-ouvertes.fr/docs/00/87/01/24/PDF/Bayes.pdf pdf] gratuit sur HAL)


* '''classics''':
* '''classics''':

Revision as of 21:44, 3 December 2013

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About statistical modeling

  • 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 (book)
    • "Bayesian Data Analysis" by Andrew Gelman (free slides, book)
    • "Mixed effects models for the population approach" by Marc Lavielle and the POPIX team at INRIA (free wiki)
    • "OpenIntro Statistics" by Diez, Barr and Cetinkaya-Rundel (free textbook)
  • 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 (free pdf, book)
  • practical, computational aspects:
    • "How to share data with a statistician" by Jeff Leek (free on procedure on GitHub)
    • "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, IPython, Matplotlib, and pandas, but 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)
  • 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)
  • philosophy, history, pragmatism:
    • "Mathematical Models and Reality: A Constructivist Perspective" by Christian Hennig (Foundations of Science 2007, 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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