IEEE

Index Tracking via Sparse Bayesian Regression and Collaborative Neurodynamic Optimization

Abstract: Index tracking is a primary passive investment strategy. Many existing methods, such as cardinality-constrained and regularized regressions, need to prespecify parameters to generate sparse ...
IEEE

Restricted Bayesian Lasso Regression With Inequality Constraints

Abstract: In this article, we investigate the utilization of the restricted Bayesian lasso regression, focusing on high-dimensional models that incorporate linear inequality constraints on the ...
Scientific Research Publishing

Gelman, A., Goodrich, B., Gabry, J. and Vehtari, A. (2019) R-Squared for Bayesian Regression Models. The American Statistician, 73, 307-309.

ABSTRACT: This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian Generalized Additive Mixed Models (GAMMs).
Hosted on MSN

Linear Regression In Python From Scratch | Simply Explained

Implement Linear Regression in Python from Scratch ! In this video, we will implement linear regression in python from scratch. We will not use any build in models, but we will understand the code ...
C&EN

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
Scientific Research Publishing

Bernardi, M., Gayraud, G. and Petrella, L. (2015) Bayesian Tail Risk Interdependence Using Quantile Regression. Bayesian Analysis, 10, 553-603.

ABSTRACT: As extremely important methods, Lp regression methods have attracted the attention of either theoretical or empirical researchers all over the world. As special cases of that, quantile and ...