Models by Example

Roll your own to understand more.

regression
machine learning
Author
Affiliation
Published

November 30, 2020

New Book

I’ve completed a new bookdown document, Models by Example, that converts most of the code from my Miscellaneous R repo. I initially just wanted to update the code, but decided to use a more formal approach to make it cleaner and more accessible. It’s mostly complete, though may be added to on rare occasion, and further cleaned as I find annoying bits here and there. Each topic contains ‘by-hand’ demonstration, such that you can see conceptually how a model is estimated, or technique employed. This can help those that want to dive a little deeper to get a peek behind the curtain of the functions and packages they use, hopefully empowering them to go further with such models.

Topics covered include the following, and I plan to post a sample chapter soon.

Models
  • Linear Regression
  • Logistic Regression
  • One-factor Mixed Model
  • Two-factor Mixed Model
  • Mixed Model via ML
  • Probit & Bivariate Probit
  • Heckman Selection
  • Marginal Structural Model
  • Tobit
  • Cox Survival
  • Hurdle Model
  • Zero-Inflated Model
  • Naive Bayes
  • Multinomial
  • Ordinal
  • Markov Model
  • Hidden Markov Model
  • Quantile Regression
  • Cubic Spline Model
  • Gaussian Processes
  • Neural Net
  • Extreme Learning Machine
  • Reproducing Kernel Hilbert Space Regression
  • Confirmatory Factor Analysis
Bayesian
  • Basics
  • Bayesian t-test
  • Bayesian Linear Regression
  • Bayesian Beta Regression
  • Bayesian Mixed Model
  • Bayesian Multilevel Mediation
  • Bayesian IRT
  • Bayesian CFA
  • Bayesian Nonparametric Models
  • Bayesian Stochastic Volatility Model
  • Bayesian Multinomial Models
  • Variational Bayes Regression
  • Topic Model
Estimation
  • Maximum Likelihood
  • Penalized Maximum Likelihood
  • L1 (lasso) regularization
  • L2 (ridge) regularization
  • Newton and IRLS
  • Nelder Mead
  • Expectation-Maximization
  • Gradient Descent
  • Stochastic Gradient Descent
  • Metropolis Hastings
  • Hamiltonian Monte Carlo

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Citation

BibTeX citation:
@online{clark2020,
  author = {Clark, Michael},
  title = {Models by {Example}},
  date = {2020-11-30},
  url = {https://m-clark.github.io/posts/2020-11-30-models-by-example/},
  langid = {en}
}
For attribution, please cite this work as:
Clark, Michael. 2020. “Models by Example.” November 30, 2020. https://m-clark.github.io/posts/2020-11-30-models-by-example/.