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

Introductory-level EDA on UN Happiness Report and World Bank Metrics from 2019

Robust estimations from distribution structures: Mean.

Robust estimations from distribution structures: Central moments.

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

In this project I have implemented 15 different types of regression algorithms including Linear Regression, KNN Regressor, Decision Tree Regressor, RandomForest Regressor, XGBoost, CatBoost., LightGBM, etc. Along with it I have also performed Hyper Paramter Optimization & Cross Validation.

regression algorithm implementaion from scratch with python (least-squares, regularized LS, L1-regularized LS, robust regression)

This is an open source library that can be used to autofocus telescopes. It uses a novel algorithm based on robust statistics. For a preprint, see https://arxiv.org/abs/2201.12466 .The library is currently used in Astro Photography tool (APT) https://www.astrophotography.app/

A collection of projects completed in STAT courses.

2021 Fall term, CSE 701 Project 03

Python implementation of RANSAC algorithm

Scikit learn compatible constrained and robust polynomial regression in Python

Robust Gaussian Process with Iterative Trimming

Generalized fiducial inference for low-dimensional robust linear regression.

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

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

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.

An implementation of the RANSAC algorithm in R.

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

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