There are many different techniques available to create a regression model. Some common techniques, listed from less complex to more complex, are: linear regression, linear lasso regression, linear ...

Gaussian process regression is a sophisticated technique that uses what is called the kernel trick to deal with complex non-linear data, and L2 regularization to avoid model overfitting where a model ...

In high-dimensional data analysis, we propose a sequential model averaging (SMA) method to make accurate and stable predictions. Specifically, we introduce a hybrid approach that combines a sequential ...

Specialization: Statistical Modeling for Data Science Applications Instructor: Brian Zaharatos, Director, Professional Master’s Degree in Applied Mathematics Prior knowledge needed: Differentiation, ...

We show the equivalence between this type of estimator and an estimator based on a linear rank test suggested by Tsiatis. This equivalence is an extension of a basic equivalence between Doob type ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

The standard linear regression model does not apply when the effect of one explanatory variable on the dependent variable depends on the value of another explanatory variable. In this case, the ...