book :: Advanced Statistical Computing

Posted by Roger D. Peng

Advanced Statistical Computing (2018) -Roger D. Peng

  1. Introduction (Textbooks vs. Computers)
  2. Solving Nonlinear Equations
    • Bisection Algorithm
    • Rates of Convergence
    • Functional Iteration
    • Newton’s Method
  3. General Optimization
    • Steepest Descent
    • The Newton Direction
    • Quasi-Newton
    • Conjugate Gradient
    • Coordinate Descent
  4. The EM Algorithm
  5. Integration
    • Laplace Approximation
  6. Independent Monte Carlo
    • Random Number Generation
    • Non-Uniform Random Numbers
    • Rejection Sampling
    • Importance Sampling
  7. Markov Chain Monte Carlo
    • Metropolis-Hastings
    • Gibbs Sampler



lecture :: PNU-winter-workshop-2019

Posted by Kipoong Kim




review :: Analyzing bagging

Posted by Kipoong Kim

(1) Analyzing bagging

(2) On bagging and Nonlinear estimation




book :: Statistical Learning with Sparsity


Statistical Learning with Sparsity (2016) The Lasso and Generalizations -Trevor Hastie -Robert Tibshirani -Martin Wainwright

  1. Introduction
  2. The Lasso for Linear Models
  3. Generalized Linear Models
  4. Generalizations of the Lasso penalty
  5. Optimization methods
  6. Statistical Inference
  7. Matrix Decompositions, Approximations, and Completion
  8. Sparse Multivariate Methods
  9. Graphs and Model Selection
  10. Signal Approximation and Compressed Sensing
  11. Theoretical Results for the Lasso



review :: elastic-net

Posted by Kipoong Kim

Regularization and variable selection via the elastic net