outlier


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outlier

[′au̇t‚lī·ər]
(geology)
A group of rocks separated from the main mass and surrounded by outcrops of older rocks.
(statistics)
In a set of data, a value so far removed from other values in the distribution that its presence cannot be attributed to the random combination of chance causes.

outlier

An entity outside the norm. Pronounced "out-liar," an outlier may refer to a person, organization or to data way outside the normal range. See anomaly detection.
References in periodicals archive ?
Jamie Burke, CEO & Founder of Outlier Ventures www.OutlierVentures.io
First, this is the only research that proposes a comprehensive multivariate outlier identification strategy, and demonstrates how to effectively detect outliers, test for their influence, and mitigate (when appropriate) the bias they cause in both cross-sectional and panel regressions.
'Black Leopard, Red Wolf' is the first acquisition since Warner Bros signed Jordan's Outlier Society to a first-look deal.
Sadly, society, teachers and parents of Pakistan have championed the abuse of potential outliers. How often do you hear 'uska deemagh kharab hogaya hai' or 'deemagh tou theek hai'.
Ramirez's inclusion is a reminder that many "outlier" artists were bound by economic, systemic, and physical circumstances that prevented their art from circulating as freely as it does now.
New Delhi [India], Apr 2 ( ANI ): Media honcho Rajeev Chandrasekhar, who recently joined Bharatiya Janata Party (BJP), has resigned from ARG Outlier Asianet News Private Limited that owns Arnab Goswami's Republic TV.
When looking at the amplitude of breaks, Japan was strongly hit through a sequence of two consecutive breaks, as an additive outlier is also detected in 2009Q1.
"Malaysia (+4 points) and the Philippines (+6 points) achieved outlier status, while Indonesia improved an incredible 15 points over the previous year," the report read.
The biggest risk to the status quo is the outlier. Challenges to existing research and research biases work best when outliers break the mold and advance into fresh territory, which is often less tainted with the entrenched problems of traditional scholarship.
in their paper [2] propose an incremental Local Outlier Factor algorithm, which is appropriate for the detecting of outliers in the data streams.
A minimum of monthly-level frequency whole-building data is required for inverse model development, outlier detection and treatment (Steps 1-3, 5).
Unfortunately, PACS is sensitive to outliers due to its dependency on the least-squares loss function which is known as very sensitive to unusual data.