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  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. Jarque–Bera test - Wikipedia

    en.wikipedia.org/wiki/Jarque–Bera_test

    The null hypothesis is a joint hypothesis of the skewness being zero and the excess kurtosis being zero. Samples from a normal distribution have an expected skewness of 0 and an expected excess kurtosis of 0 (which is the same as a kurtosis of 3). As the definition of JB shows, any deviation from this increases the JB statistic.

  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. 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 ...

  7. Multimodal distribution - Wikipedia

    en.wikipedia.org/wiki/Multimodal_distribution

    where b 2 is the kurtosis and b 1 is the square of the skewness. Equality holds only for the two point Bernoulli distribution or the sum of two different Dirac delta functions. These are the most extreme cases of bimodality possible. The kurtosis in both these cases is 1. Since they are both symmetrical their skewness is 0 and the difference is 1.

  8. These 89 Appetizers Might Just Be The Best Part Of ... - AOL

    www.aol.com/89-appetizers-might-just-best...

    Turkey Cheese Ball. Even if you're not serving turkey this Thanksgiving doesn't mean you can't get in on the theme. Enter: this adorable cheeseball.We used carrots, pecans, pretzels, and bell ...

  9. Cokurtosis - Wikipedia

    en.wikipedia.org/wiki/Cokurtosis

    Let X and Y each be normally distributed with correlation coefficient ρ. The cokurtosis terms are (,,,) = +(,,,) = (,,,) =Since the cokurtosis depends only on ρ, which is already completely determined by the lower-degree covariance matrix, the cokurtosis of the bivariate normal distribution contains no new information about the distribution.