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Related Concept Videos

Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
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Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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Related Experiment Videos

Extreme Rare Events Identification Through Jaynes Inferential Approach.

Yair Neuman1, Yochai Cohen2, Eden Erez3

  • 1The Department of Cognitive and Brain Sciences, The Zlotowski Center for Neuroscience, and The Data Science Research Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

Big Data
|October 14, 2021
PubMed
Summary
This summary is machine-generated.

Identifying extreme rare events is challenging. This study introduces a new feature engineering methodology based on Jaynes

Keywords:
Jaynesextreme rare eventsfeature engineeringinferencepulp-and-paper

Related Experiment Videos

Area of Science:

  • Data Science
  • Machine Learning
  • Industrial Engineering

Background:

  • Extreme rare events pose significant challenges in various real-world applications.
  • These include identifying solo perpetrators and predicting industrial production failures.
  • Existing methodologies often struggle with the inherent difficulties of detecting infrequent occurrences.

Purpose of the Study:

  • To introduce a novel methodology for the identification of extreme rare events.
  • To frame this methodology as a feature engineering approach.
  • To address the practical challenges in contexts with measurable risks.

Main Methods:

  • The study employs Jaynes' inferential approach as the foundation for the proposed methodology.
  • This approach is applied to feature engineering for rare event detection.
  • The methodology is tested using a dataset from the pulp-and-paper industry.

Main Results:

  • The new methodology demonstrates effectiveness in identifying extreme rare events within the tested dataset.
  • The application in the pulp-and-paper industry highlights practical utility.
  • The results indicate improved feature engineering for risk assessment.

Conclusions:

  • The proposed methodology, based on Jaynes' inferential approach, offers a valuable tool for feature engineering.
  • It provides a robust solution for identifying extreme rare events in industrial settings.
  • The approach has significant implications for practical risk management and prediction.