Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

A semi-parametric generalization of the proportional hazards regression model is defined, whereby the hazard functions can cross for different values of the covariates. In the two-sample comparison, ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demo of Poisson regression, where the goal is to predict a count of things arriving, such as the number of telephone calls ...

In this paper we propose a semi-parametric, parsimonious value-at-risk forecasting model based on quantile regression and readily available market prices of option contracts from the over-the-counter ...

A tool called AI-Newton can derive scientific laws from raw data, but is some way from developing human-like reasoning.

This article ( original research paper) proposes a systematic regression-based fundamental equity valuation model that can potentially be applied in areas such as quantitative finance and machine ...

It can be highly beneficial for companies to develop a forecast of the future values of some important metrics, such as demand for its product or variables that describe the economic climate. There ...