Location of Several Outliers in Multiple-Regression Data Using Elemental Sets Douglas M. Hawkins, Dan Bradu and Gordon V. Kass Technometrics , pp. 197-208 (12 pages) ...

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 ...

Methods We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each ...

As data sets get bigger and the variety of features grows, it becomes harder to detect outliers. “Everything is trickier here, it’s hard to compute averages and look at what kinds of outliers you ...

Explore the importance of robust statistics like median and MAD in data analysis, ensuring accurate insights despite outliers ...

That gives QR several advantages over LR, including a higher degree of stability when confronting outliers. As a simple example, consider two sets of numbers – Data A and Data B.

In a world of large data sets, techniques such as winsorisation can be used to handle outliers by keeping the observation but reducing its influence on aggregate metrics like value weighted ...

Outliers deviate from the norm. But outliers can tell us more about our data, how we gather it, and what is in it, if we examine the data set carefully.

New integrations unlock more valuable customer data for richer analysisOAKLAND, Calif., Oct. 07, 2020 (GLOBE NEWSWIRE) -- With its automated business analysis (ABA) platform, Outlier discovers and ...

Outlier, which offers a business analysis platform that extracts data and analyzes it to spot changes in behavior, has raised $22.1 million in funding.