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Fast diffusion kurtosis imaging in acute ischemic stroke shows mean kurtosis‐diffusivity mismatch
Journal of Neuroimaging, 2022Abstract Background and Purpose Diffusion kurtosis imaging (DKI) is an advanced technique more specific to irreversible ischemic injury than conventional diffusion‐weighted imaging (DWI). However, its clinical translation has been limited by a long acquisition time and complex postprocessing.
Ranliang Hu +4 more
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Performance analysis of the augmented complex-valued least mean kurtosis algorithm
Signal Processing, 2023Jingen Ni, Zhe Li, Engin Cemal Menguc
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Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2016
Diffusion kurtosis imaging can provide a better understanding of microstructural white matter (WM) changes where crossing fibers exist, compared with conventional diffusion tensor imaging. Here, we aimed to examine the differences of mean kurtosis (MK) and fractional anisotropy (FA) values between patients with schizophrenia and control subjects using ...
Hisashi Narita +2 more
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Diffusion kurtosis imaging can provide a better understanding of microstructural white matter (WM) changes where crossing fibers exist, compared with conventional diffusion tensor imaging. Here, we aimed to examine the differences of mean kurtosis (MK) and fractional anisotropy (FA) values between patients with schizophrenia and control subjects using ...
Hisashi Narita +2 more
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Ameliorate estimation of mean using skewness and kurtosis of auxiliary character
Journal of Statistics and Management Systems, 2022R R Sinha
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Network Traffic Prediction Using Least Mean Kurtosis
IEICE Transactions on Communications, 2006Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management.
Hong Zhao +2 more
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Mean-Variance-Skewness-Kurtosis-based Portfolio Optimization
First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06), 2006In the mean-variance-skewness-kurtosis framework, this study solve multiple conflicting and competing portfolio objectives such as maximizing expected return and skewness and minimizing risk and kurtosis simultaneously, by construction of a polynomial goal programming (PGP) model into which investor preferences over higher return moments are ...
Kin Keung Lai, Lean Yu, Shouyang Wang
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Fast acquisitions for mean kurtosis and microscopic fractional anisotropy
ISMRM Annual Meeting, 2023We propose two acquisition protocols that minimize the number of directions needed for estimating MD, FA, MK, and $$$\mu$$$FA. Using symmetric trace-free decompositions of the diffusion, kurtosis, and covariance tensors, we design acquisitions that cancel unnecessary tensor elements, thereby reducing the number of directions needed to generate the maps.
Santiago Coelho +3 more
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The Meaning of Kurtosis: Darlington Reexamined
The American Statistician, 1986Abstract There seems to be no universal agreement about the meaning and interpretation of kurtosis. An easy interpretation is given here: kurtosis is a measure of dispersion around the two values μ ± σ.
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Four improved statistics for contrasting means by correcting skewness and kurtosis
British Journal of Mathematical and Statistical Psychology, 2005This paper is concerned with removing the influence of non‐normality in the classical t ‐statistic for contrasting means. Using higher‐order expansion to quantify the effect of non‐normality, four corrected statistics are provided.
Hirokazu, Yanagihara, Ke-Hai, Yuan
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Stochastic computation of moments, mean, variance, skewness and kurtosis
Electronics Letters, 2015 Stochastic computation of statistical moments and related quantities, such as the mean, variance, skewness and kurtosis, is performed with simple neural networks. The computed quantities can be used to estimate the parameters of input data probability distributions, gauge the normality of data, add useful features to the inputs, preprocess data and for
G.M. Georgiou, K. Voigt
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