Outliers deviate from the norm—significantly enough to give marketers pause. But outliers can tell us more about our data, how we gather it, and what is in it, if we examine the entire data set ...

Development and Validation of a Machine Learning Approach Leveraging Real-World Clinical Narratives as a Predictor of Survival in Advanced Cancer Artificial intelligence (AI) models for medical image ...

Outliers have the potential to skew analysis when they aren’t properly accounted for. Addressing outliers, specifically in trade cost analysis (TCA) data, is crucial for traders because it ensures the ...

This paper analyzes the resampling technique of jackknifing and its capability of detecting outliers in data envelopment analysis. It is well recognized that measured efficiency is sensitive to ...

In my last few articles, I've looked at a number of ways machine learning can help make predictions. The basic idea is that you create a model using existing data and then ask that model to predict an ...

Data analytics deals with making observations with various data sets, and trying to make sense of the data. When dealing with very large data sets, automated tools must be used to find patterns and ...

image: The PM2.5 monitoring instruments at State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences.

Heart rate variability (HRV) measurements are increasingly used by researchers to describe physiology, provide prognostic information, and measure response to therapies (1). HRV is the beat-to-beat ...