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

From OpenWetWare

(Difference between revisions)
Jump to: navigation, search
(About statistical modeling: add links Leek, Kass, litterature)
(About statistical modeling: add link Lavielle)
Line 12: Line 12:
** "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])
* '''mathematical aspects''':
* '''mathematical aspects''':

Revision as of 18:24, 30 November 2013

Project name Main project page
Previous entry      Next entry

About statistical modeling

  • great 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)
  • 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 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: Emacs
  • 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" by Christian Robert (chapitre 2013, free pdf on HAL)
  • classics:
    • list from Christian Robert


Personal tools