Kurtosis - an overview | ScienceDirect Topics
Kurtosis is a descriptive statistic used to help measure how data disperse between a distribution's center and tails, with larger values indicating a data distribution may have “heavy” tails that are thickly concentrated with observations or that are long with extreme observations. A distribution's kurtosis can be calculated using different ...
Kurtosis - an overview | ScienceDirect Topics
2010年2月6日 · Kurtosis is the degree of peakedness of a distribution, usually taken relative to a normal distribution. A distribution having a relatively high peak such as the curve of Fig. 3.2(a) is called leptokurtic, while the curve of Fig. 3.2(b), which is flat-topped, is called platykurtic.
Kurtosis - an overview | ScienceDirect Topics
where k is kurtosis, µ is the average of data and σ is the standard deviation, and n is the amount of data. The user should be aware that kurtosis must not be confused with peakedness. “A distribution can be infinitely peaked with low kurtosis, and a distribution can be perfectly flat-topped with infinite kurtosis.
Kurtosis - an overview | ScienceDirect Topics
Kurtosis (or, more accurately, the excess kurtosis) is a mathematical term that measures how much a distribution deviates from being Gaussian. Kurtosis can be measured directly from an estimated displacement function, though another way to estimate it that requires fewer measurements is to deconstruct the signal from multiple b-values and ...
Kurtosis - an overview | ScienceDirect Topics
A kurtosis of zero is obtained for scores from a normal distribution (since we subtract the value of 3 in the kurtosis formula). A negative kurtosis corresponds to a platykurtic, or wide, distribution with more extreme scores than expected in the normal. Such a distribution would be wider and thicker in the tails.
Detecting and displaying size bimodality: Kurtosis, skewness and ...
1992年9月7日 · Kurtosis of log-transformed data might then be an index of distribution bimodalizability, provided their skewness is close to zero. Ford (1975) and Rabinowitz (1979) used kurtosis in such a manner: showing the non-significance of g~ of transformed data (approximately symmetric distribution) they quantified bimodality with the g2 coefficient.
Diffusion Kurtosis Imaging in Diagnosing Parkinson's Disease: A ...
2024年8月9日 · Kurtosis metrics, capable of estimating microstructural complexity, are valuable in DKI, with MK being a commonly employed metric due to its spatial independence. Wang et al. (9) initially applied DKI to assess microstructural alterations in PD and found the clinical utility of MK value in PD classification, achieving an AUC of 0.95.
Spectral kurtosis for fault detection, diagnosis and prognostics of ...
2016年1月1日 · Kurtosis is a measure of peakedness, and hence it is a good indicator of signal impulsiveness in the context of fault detection for rotating components. Kurtosis is expressed as (1) k u r t o s i s (x) = E {(x − μ) 4} σ 4 − 3 where μ and σ are the mean and standard deviation of time series x, respectively, while E {⋅} is the ...
Maximum L-Kurtosis deconvolution and frequency-domain …
Aiming at solving the limitation, a novel maximum L-Kurtosis deconvolution (MLKD) algorithm is proposed. Firstly, the L-Kurtosis is introduced, and the results show that it remains approximately unchanged for different outliers. This characteristic of the L-Kurtosis is the keystone for the proposed deconvolution algorithm.
Optimization of spectral kurtosis-based filtering through flow ...
Additionally, the spectral kurtosis exhibits sensitivity to random impulses, given its inverse correlation with the likelihood of their occurrence. Also, spectral kurtosis is estimated as a data-driven method. The spectral kurtosis obtained for non-Gaussian noise is bad as there is no target. Thus, setting this as an initial value has no sense.