Definitions for

**"Kurtosis"****Related Terms:**Normal distribution, Gaussian distribution, Probability distribution, Gaussian, Distribution, Uniform distribution, Variance, Probability density function, Skewness, Central tendency, Outlier, Poisson distribution, Standard deviation, Central limit theorem, Variability, Bell curve, Random error, Uncertainty, Binomial distribution, Coefficient of variation, Lognormal distribution, Spatial autocorrelation, Skewed distribution, Chi-square test, Stochastic model, Confidence level, Outliers, Deviation, Correlation coefficient, Effect size, Deviation, Confidence interval, Homogeneity, Significant, Variate, Correlation, Random variable, Sep, P value, Discriminant analysis, Statistic, Degrees of freedom, Dispersion, Statistically significant, Homoscedasticity, Least squares, Linear regression, Skewed, Statistical significance, R squared

The relative concentration of scores in the center, the upper and lower tails, and the shoulders of a distribution.

A quality measure of the peakedness of a data distribution.

Measures the flatness of the tails of a distribution curve. A flat-tailed distribution has an increased chance of large positive or negative realizations. This indicator give a good picture of the dramatic swings in some hedge funds.

Measures the degree of distribution from both sides of a "Bell Curve" For a risk-averse investor like the Van Arbor Funds, a low kurtosis is preferable (returns not far away from the mean).

The peakedness of a distribution. Leptokurtic is more peaked, Mesokurtic is a normal distribution, and Platykurtic is a flatter distribution.

Kurtosis indicates the extent to which a distribution is more peaked or flat-topped than a normal distribution.

Kurtosis (the term first used by Pearson, 1905) measures the "peakedness" of a distribution. If the kurtosis is clearly different than 0, then the distribution is either flatter or more peaked than normal; the kurtosis of the normal distribution is 0.

(Ku): The degree of the curvature of a unimodal frequency distribution. It is the peakedness or flatness of the curve. M-N-O-P

Kurtosis is a measure of how outlier-prone a distribution is. The kurtosis of the normal distribution is 3. Distributions that are more outlier-prone than the normal distribution have kurtosis greater than 3; distributions that are less outlier-prone have kurtosis less than 3.

Kurtosis characterises the relative peakedness or flatness of a distribution compared with the normal distribution. Positive kurtosis indicates a relatively peaked distribution whilst negative kurtosis indicates a relatively flat distribution !-- if ( navigator.userAgent.toLowerCase().indexOf('mozilla') != -1 && navigator.userAgent.indexOf('5.') == -1 && navigator.userAgent.indexOf('6.') == -1 ) document.write(' '); else document.write('/tdtd /tdtd valign="top" nowrap="nowrap"div style="position:relative"div style="position:absolute"/divdiv style="position:absolute;left:3px;top:-1px"/div/div'); //-- sophos9 Site Admin Joined: 08 Feb 2006 Posts: 117 Location: UK

Measures the fatness of the tails of a probability distribution. A fat-tailed distribution has higher-than-normal chances of a big positive or negative realization. Kurtosis should not be confused with skewness, which measures the fatness of one tail. Kurtosis is sometimes referred to as the volatility of volatility.

( Peakedness) is a property of distribution of a random variables computed as forth central moment and characterize how peaked (positive kurtosis or leptokurtic) or flat (negative kurtosis or platykurtic) the distribution is. [pg 32, 2

A measure of the degree of peakedness and outlier-proneness of a distribution. Kurtosis can be positive or negative. The standard normal distribution has a kurtosis of zero and is the standard against which the kurtosis values for other distributions are measured. The larger the kurtosis, the more peaked the distribution and the more frequent the outliers. The smaller the kurtosis the flatter the distribution.

A parameter describing the peakedness and tails of a probability distribution.

is a measure of the "peakedness" of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations, as opposed to frequent modestly-sized deviations. likelihood function (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example, imagine pulling a numbered ball with the number k from a bag of n balls, numbered 1 to n. Then you could describe a likelihood function for the random variable N as the probability of getting k given that there are n balls : the likelihood will be 1/n for n greater or equal to k, and 0 for n smaller than k. Unlike a probability distribution function, this likelihood function will not sum up to 1 on the sample space.

Descriptive measure of how flat or pointed a distribution is.

A statistical measure used to describe the distribution of observed data around the mean.

In probability theory and statistics, kurtosis is a measure of the "peakedness" of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations, as opposed to frequent modestly-sized deviations.