Noun:

সূঁচালতা,

kurtosis's Usage Examples:

In probability theory and statistics, kurtosis (from Greek: κυρτός, kyrtos or kurtos, meaning "curved, arching") is a measure of the "tailedness" of the.

α → 0 excess kurtosis = lim β → 0 excess kurtosis = lim μ → 0 excess kurtosis = lim μ → 1 excess kurtosis = ∞ lim α → ∞ excess kurtosis = 6 β ,   lim.

standardized moment is the skewness, and the fourth standardized moment is the kurtosis.

is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution.

analogous to standard deviation, skewness and kurtosis, termed the L-scale, L-skewness and L-kurtosis respectively (the L-mean is identical to the conventional.

"J-shaped", or numerically, using quantitative measures such as skewness and kurtosis.

moments, using the method of moments, as in the skewness (3rd moment) or kurtosis (4th moment), if the higher moments are defined and finite.

deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness.

Gaussian signals have zero kurtosis, Super-Gaussian signals have positive kurtosis, and Sub-Gaussian signals have negative kurtosis.

skewness and kurtosis.

Statistics ' Probability Letters 8:297-299 Klaassen CAJ, Mokveld PJ, van Es B (2000) Squared skewness minus kurtosis bounded by 186/125.

In statistics and decision theory, kurtosis risk is the risk that results when a statistical model assumes the normal distribution, but is applied to observations.

resembles the normal distribution in shape but has heavier tails (higher kurtosis).

The test is based on transformations of the sample kurtosis and skewness, and has power only against the alternatives that the distribution.

{\gamma ^{2}+1}{\kappa }}} where γ is the skewness and κ is the kurtosis.

The kurtosis is here defined to be the standardised fourth moment around the.

deviation but different means, the overall distribution will exhibit low kurtosis relative to a single normal distribution – the means of the sub populations.

deviation a measure of the shape of the distribution like skewness or kurtosis if more than one variable is measured, a measure of statistical dependence.

distribution is a probability distribution that exhibits a large skewness or kurtosis, relative to that of either a normal distribution or an exponential distribution.

For a symmetric variance-gamma distribution, the kurtosis can be given by 3 ( 1 + 1 / λ ) {\displaystyle 3(1+1/\lambda )} .

It is a combination of kurtosis risk and skewness risk: overall returns are dominated by extreme events (kurtosis), which are to the downside (skew).