Econometrics Navigator
0.0.7
  • 1. Concepts and techniques
    • Analysis of variance (ANOVA)
    • Bias-variance tradeoff
    • Bootstrap
    • Causation, causality
    • Central limit theorem, CLT
    • Derivative
    • Maximum likelihood
    • Mode
    • Ordinary least squares, OLS
    • Principal components analysis, PCA
    • Simulation
  • 2. Textbooks and courses
  • 3. The science of teaching
  • Data
  • Software
  • Good clues from Twitter
  • Blogs
  • Acronyms
  • Changelog
Econometrics Navigator
  • »
  • 1. Concepts and techniques »
  • Principal components analysis, PCA
  • View page source

Principal components analysis, PCAΒΆ

Math:

Assumptions:

Usual steps:

What may go wrong:

Discussion:

Replication examples:

Links:

  • https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues/2700#2700

  • https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_3d.html#sphx-glr-auto-examples-decomposition-plot-pca-3d-py

  • https://stats.stackexchange.com/questions/48214/replicating-shalizis-new-york-times-pca-example?rq=1

Projection, rejection, PCA:

  • https://stackoverflow.com/questions/52288029/function-that-computes-projection-and-recostruction-error-using-numpy-python/52290082#52290082

  • http://www.cs.cmu.edu/~guestrin/Class/15781/slides/pca-mdps-annotated.pdf

  • https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/least-squares-determinants-and-eigenvalues/projections-onto-subspaces/MIT18_06SCF11_Ses2.2sum.pdf

Next Previous

© Copyright 2018-2019, Evgeny Pogrebnyak

Built with Sphinx using a theme provided by Read the Docs.