robust-regresssion

RANSAC Template Library

Solve many kinds of least-squares and matrix-recovery problems

MATLAB implementation "Provable Dynamic Robust PCA or Robust Subspace tracking", IEEE Transactions on Information Theory, 2019.

Robust Gaussian Process with Iterative Trimming

ML Coursework focused on solving Computational Finance and Risk Assessment models

This is the implementation of the five regression methods Least Square (LS), Regularized Least Square (RLS), LASSO, Robust Regression (RR) and Bayesian Regression (BR).

Scikit learn compatible constrained and robust polynomial regression in Python

R Package implementing the Penalized Elastic Net S- and MM-Estimator for Linear Regression

This is an open source library that can help to autofocus telescopes. The library fits half flux diameter data to a hyperbola, from which the correct focus point can be interpolated. The library uses various robust statistical outlier detection methods. It is currently used in the software Astro Photography tool (APT) from https://www.astrophotography.app/

In this repository, using the statistical software R, are been analyzed robust techniques to estimate multivariate linear regression in presence of outliers, using the Bootstrap, a simulation method where the construction of sample distribution of given statistics occurring through resampling the same observed sample.

Code accompanying the paper "Globally Optimal Learning for Structured Elliptical Losses", published at NeurIPS 2019

Applied analysis on the Bayesian student-t "Robust" regression model with Jeffrey's prior. Compared its model performance and robustness of posterior distributions with the Gaussian model when outliers are present.

Regression for Boston Housing price prediction: Linear, Multiple, Robust, OLS, Regularization (Ridge-l1 norm, LASSO-l2 norm, ElasticNet)

A collection of projects completed in STAT courses.

An implementation of the RANSAC algorithm in R.

Generalized fiducial inference for low-dimensional robust linear regression.