Learning tasks with orthogonal/disjoint supports
-
Updated
Mar 10, 2017 - R
Learning tasks with orthogonal/disjoint supports
Raw files for a document providing an overview of mixed models from varying perspectives.
Flexible SVM framework implementation
Nonparametric regression and prediction using the highly adaptive lasso algorithm
Repo to keep track of work done in Dr. Robert McCulloch's Graduate Machine Learning course
Experiments for Binarsity: a penalization for one-hot encoded features
A Julia module that implements the (normalized) iterative hard thresholding algorithm(IHT) of Blumensath and Davies. IHT performs feature selection akin to LASSO- or MCP-penalized regression using a greedy selection approach.
An investigation into why on-ground students choose to take online equivalents of their in-person courses
an extremely basic Julia implementation of the Orthogonalizing EM (OEM) algorithm for penalized regression
An exploration in how discussion forum data can be used to measure faculty engagement and its effect on student outcomes
Bayesian regression with spike and slab prior. Inference with Gibbs sampling.
A workshop on using generalized additive models and the mgcv package.
Multi-source sparse Tweedie modelling
approximate message massing (AMP), bridge regression, optimal tuning
Biomarker selection in penalized regression models
Regression models for "epigenetic clock" estimation of canine chronological age
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
University of Utah—MKTG 6600: Business Algorithms | Taken: Fall 2020
Interactive Notebook demonstrating the R-library bigtime
My research
Add a description, image, and links to the penalized-regression topic page so that developers can more easily learn about it.
To associate your repository with the penalized-regression topic, visit your repo's landing page and select "manage topics."