shrinkage

A collaborative repository highlighting Bayesian autoregressive analysis with extensions. It is prepared by the students of Macroeconometrics at the University of Melbourne.

Building blocks for trend-following CTA strategies: scale-free oscillators, volatility-adjusted returns, matrix shrinkage and a position engine

Experiments with experimental rule-based models to go along with imodels.

R package for adaptive correlation and covariance matrix shrinkage.

This repository contains data and code relative to the manuscript "A large covariance matrix estimator under intermediate spikiness regimes" by Matteo Farnè and Angela Montanari (https://arxiv.org/abs/1711.08950).

Privacy-preserving hierarchical Bayes for multi-tenant fleets: empirical-Bayes partial pooling, cold-start mitigation, zero-dependency TypeScript.

R package for Dirichlet adaptive shrinkage and smoothing

R code and BayesX scripts for effect selection and complexity reduction on basis of the Normal Beta Prime Spike and Slab Prior

Tool combining pngcrush and optipng to optimize input PNG

Sliding Filter for AWGN Denoising

My Master's thesis on Bayesian Classification with Regularized Gaussian Models

Final Project from the course - Computational Statistics (Summer Term, 2020), University of Bonn

FEFO bottleneck and shrinkage diagnostic for a fresh-produce processing centre, as a live Power Query + PivotTable workbook over a simulated operations week. Causal attribution via a counterfactual re-simulation, not correlation.

Word Enrichment Analysis using VEctor Representations

Introduction to Data Mining

Horseshoe regression model fitted in PyMC.

Base saturation percentage determination using shrinkage method. Due to the multicollinearity issue, we chose shrinkage/penalized/regularized regression. Since, we have small number of samples, we had no luxury of having separate test set of data, so we did iterated k-Fold cross validation.

Rolling backtest comparing sample vs Ledoit-Wolf covariance in max-Sharpe and min-variance portfolio optimization.

An R package for adaptive shrinkage

Code for the paper E. Raninen and E. Ollila, "Bias Adjusted Sign Covariance Matrix," in IEEE Signal Processing Letters, vol. 29, pp. 339-343, 2022, doi: 10.1109/LSP.2021.3134940.