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  2. Heavy-tailed distribution - Wikipedia

    en.wikipedia.org/wiki/Heavy-tailed_distribution

    The distribution of a random variable X with distribution function F is said to have a long right tail [1] if for all t > 0, [> + >] =,or equivalently ¯ (+) ¯ (). This has the intuitive interpretation for a right-tailed long-tailed distributed quantity that if the long-tailed quantity exceeds some high level, the probability approaches 1 that it will exceed any other higher level.

  3. Skewness - Wikipedia

    en.wikipedia.org/wiki/Skewness

    negative skew: The left tail is longer; the mass of the distribution is concentrated on the right of the figure. The distribution is said to be left-skewed, left-tailed, or skewed to the left, despite the fact that the curve itself appears to be skewed or leaning to the right; left instead refers to the left tail being drawn out and, often, the ...

  4. Fat-tailed distribution - Wikipedia

    en.wikipedia.org/wiki/Fat-tailed_distribution

    A fat-tailed distribution is a probability distribution that exhibits a large skewness or kurtosis, relative to that of either a normal distribution or an exponential distribution. [ when defined as? ] In common usage, the terms fat-tailed and heavy-tailed are sometimes synonymous; fat-tailed is sometimes also defined as a subset of heavy-tailed.

  5. Category:Tails of probability distributions - Wikipedia

    en.wikipedia.org/wiki/Category:Tails_of...

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  6. Long tail - Wikipedia

    en.wikipedia.org/wiki/Long_tail

    In statistics, the term long-tailed distribution has a narrow technical meaning, and is a subtype of heavy-tailed distribution. [2] [3] [4] Intuitively, a distribution is (right) long-tailed if, for any fixed amount, when a quantity exceeds a high level, it almost certainly exceeds it by at least that amount: large quantities are probably even ...

  7. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    The standard measure of a distribution's kurtosis, originating with Karl Pearson, [1] is a scaled version of the fourth moment of the distribution. This number is related to the tails of the distribution, not its peak; [ 2 ] hence, the sometimes-seen characterization of kurtosis as " peakedness " is incorrect.

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  9. Long-tail traffic - Wikipedia

    en.wikipedia.org/wiki/Long-tail_traffic

    Heavy-tail distributions have properties that are qualitatively different from commonly used (memoryless) distributions such as the exponential distribution. The Hurst parameter H is a measure of the level of self-similarity of a time series that exhibits long-range dependence, to which the heavy-tail distribution can be applied.