enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    For a sample of n values, a method of moments estimator of the population excess kurtosis can be defined as = = = (¯) [= (¯)] where m 4 is the fourth sample moment about the mean, m 2 is the second sample moment about the mean (that is, the sample variance), x i is the i th value, and ¯ is the sample mean.

  3. Kurtosis risk - Wikipedia

    en.wikipedia.org/wiki/Kurtosis_risk

    Kurtosis risk applies to any kurtosis-related quantitative model that assumes the normal distribution for certain of its independent variables when the latter may in fact have kurtosis much greater than does the normal distribution. Kurtosis risk is commonly referred to as "fat tail" risk. The "fat tail" metaphor explicitly describes the ...

  4. Higher-order statistics - Wikipedia

    en.wikipedia.org/wiki/Higher-order_statistics

    HOS are particularly used in the estimation of shape parameters, such as skewness and kurtosis, as when measuring the deviation of a distribution from the normal distribution. In statistical theory , one long-established approach to higher-order statistics, for univariate and multivariate distributions is through the use of cumulants and joint ...

  5. D'Agostino's K-squared test - Wikipedia

    en.wikipedia.org/wiki/D'Agostino's_K-squared_test

    In the following, { x i } denotes a sample of n observations, g 1 and g 2 are the sample skewness and kurtosis, m j ’s are the j-th sample central moments, and ¯ is the sample mean. Frequently in the literature related to normality testing, the skewness and kurtosis are denoted as √ β 1 and β 2 respectively.

  6. Jarque–Bera test - Wikipedia

    en.wikipedia.org/wiki/Jarque–Bera_test

    In statistics, the Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The test is named after Carlos Jarque and Anil K. Bera. The test statistic is always nonnegative. If it is far from zero, it signals the data do not have a normal distribution.

  7. Multimodal distribution - Wikipedia

    en.wikipedia.org/wiki/Multimodal_distribution

    The kurtosis is here defined to be the standardised fourth moment around the mean. The value of b lies between 0 and 1. [26] The logic behind this coefficient is that a bimodal distribution with light tails will have very low kurtosis, an asymmetric character, or both – all of which increase this coefficient. The formula for a finite sample ...

  8. These Birds are Interior Design Experts - AOL

    www.aol.com/birds-interior-design-experts...

    Forget Chip and Joanna Gaines, bowerbirds are the real home renovation stars! These incredible avian artists have a keen eye for color and are masters of interior design, spending countless hours ...

  9. L-moment - Wikipedia

    en.wikipedia.org/wiki/L-moment

    For instance, the Laplace distribution has a kurtosis of 6 and weak exponential tails, but a larger 4th L-moment ratio than e.g. the student-t distribution with d.f.=3, which has an infinite kurtosis and much heavier tails. As an example consider a dataset with a few data points and one outlying data value.