In today's data-rich environment, business are always looking for a way to capitalize on available data for new insights and ...

Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology This study used SEER data from 1975 to 2018 and included 545,486 patients with lung ...

This is the twelfth in a series of lecture notes which, if tied together into a textbook, might be entitled “Practical Regression.” The purpose of the notes is to supplement the theoretical content of ...

Categorical-or qualitative-time series data with random time-dependent covariates are frequently encountered in diverse applications as the list of examples shows. As with "ordinary" time series, the ...

Simply collecting data is not enough. You can fill spreadsheets with data, but it's useless if you can't act on it. Regression is one of the most powerful statistical tools for finding relationships ...

This paper provides an alternative approach to penalized regression for model selection in the context of high-dimensional linear regressions where the number of covariates is large, often much larger ...

Time series forecasts are used to predict a future value or a classification at a particular point in time. Here’s a brief overview of their common uses and how they are developed. Industries from ...

Engineers have demonstrated a simple computational approach for supporting the classification performance of neural networks operating on sensor time series. The proposed technique involves feeding ...