The presence of outliers has serious adverse effects on the modeling and forecasting of functional data. Therefore, outlier detection, aiming at identifying abnormal functional curves from a dataset, ...

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 ...

Robust estimation and outlier detection play a critical role in modern data analysis, particularly when dealing with high-dimensional datasets. In such contexts, classical statistical methods often ...

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 ...

With increasing focus on quality and reliability across all segments beyond just automotive, medical and mil-aero, it is more critical than ever for companies to leverage every byte of test data at ...

The next wave of automotive chips for assisted and autonomous driving is fueling the development of new approaches in a critical field called outlier detection. KLA-Tencor, Optimal+, as well as Mentor ...

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 ...

New Relic unveils Agentic AI Monitoring and MCP Server to modernize AI observability, enabling enterprises to optimize agent ...