How can you tell if your regression model suffers from endogeneity? One way is to use a formal test, such as the Durbin-Wu-Hausman test, which compares the OLS estimator with an alternative ...

Sometimes referred to as Adaline models, these perceptrons do not have an activation function. This way, their output is directly considered the regression result for a given input value. Note that ...

Researchers at EPFL have created a mathematical model that helps explain how breaking language into sequences makes modern AI ...

RegMix follows a four-step process to optimize LLM training: Generate Configs: Create various different data mixture configurations. Train Small Models: Use these configs to train small "proxy" models ...

Gaussian process (GP) regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in function estimation. In the context of ...

Separate binary regression models When the dependent variable is ordinal, such as here, we can choose to dichotomise it and use binary logistic regression. A variable with three categories can be ...

In these cases, OLS should not be used and the literature suggests carrying out orthogonal distance regression (ODR). 5,6 The aim of this work is to suggest how to handle such cases in which errors in ...

Accurate forecast of the hourly spot price of electricity plays a vital role in energy trading decisions. However, due to the complex nature of the power system, coupled with the involvement of ...

We ran an ordinary least squares (OLS) regression model of the numeric Moody’s Credit Rating on the 10 variables. We used the coefficient for each variable as the relative weight of that variable in ...