Results 41 to 50 of about 16,146 (162)

Prognostic ¹⁸F-FDG Radiomic Features in Advanced High-Grade Serous Ovarian Cancer

Diagnostics, 2023
High-grade serous ovarian cancer (HGSOC) is an aggressive disease with different clinical outcomes and poor prognosis. This could be due to tumor heterogeneity. The 18F-FDG PET radiomic parameters permit addressing tumor heterogeneity. Nevertheless, this
Daniela Travaglio Morales, Carlos Huerga Cabrerizo, Itsaso Losantos García, Mónica Coronado Poggio, José Manuel Cordero García, Elena López Llobet, Domenico Monachello Araujo, Sebastián Rizkallal Monzón, Luis Domínguez Gadea   +8 more
doaj   +1 more source

An Improved Dual-Kurtogram-Based T2 Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

Shock and Vibration, 2021
Condition monitoring and compound fault diagnosis are crucial key points for ensuring the normal operation of rotating machinery. A novel method for condition monitoring and compound fault diagnosis based on the dual-kurtogram algorithm and multivariate ...
Zhiyuan Jiao, Wei Fan, Zhenying Xu
doaj   +1 more source

A family of kurtosis orderings for multivariate distributions

Journal of Multivariate Analysis, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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PREDICTING THE DEPOSITIONAL ENVIRONMENTS AND TRANSPORTATION MECHANISMS OF SEDIMENTS USING GRANULOMETRIC PARAMETERS, BIVARIATE AND MULTIVARIATE ANALYSES

GeoScience Engineering, 2019
Grain size distribution and classes present in sedimentary rocks are responsive to the physical changes of the transporting media and the basin of deposition.
P. R. IKHANE, O.V. OLADIPO, O.O. OYEBOLU
doaj   +1 more source

A Treatment of Multivariate Skewness, Kurtosis, and Related Statistics

Journal of Multivariate Analysis, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

STAR_outliers: a python package that separates univariate outliers from non-normal distributions

BioData Mining, 2023
There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers.
John T. Gregg, Jason H. Moore
doaj   +1 more source

Desempenho de testes de normalidade multivariados avaliado por simulação Monte Carlo Monte Carlo evaluation of the performance of multivariate normality tests

Ciência e Agrotecnologia, 2007
Neste trabalho, objetivou-se avaliar a performance do teste multivariado de normalidade de Shapiro-Wilk implementado no R comparando o seu desempenho com os testes de assimetria e curtose de Mardia (1970, 1974, 1975) utilizando simulação Monte Carlo ...
Narjara Fonseca Cantelmo, Daniel Furtado Ferreira   +1 more
doaj   +1 more source

Monoexponential, biexponential and diffusion kurtosis MR imaging models: quantitative biomarkers in the diagnosis of placenta accreta spectrum disorders

BMC Pregnancy and Childbirth, 2022
Background To investigate the diagnostic value of monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in differentiating placenta accreta spectrum (PAS) disorders. Methods A total of 65 patients with PAS disorders and 27 patients with
Tao Lu, Yishuang Wang, Aiwen Guo, Wei Cui, Yazheng Chen, Shaoyu Wang, Guotai Wang   +6 more
doaj   +1 more source

Predictive value of ADC histogram analysis in 2 cm peritumoral edema zone for spatial pattern of recurrence in glioblastoma

陆军军医大学学报, 2023
Objective To evaluate the predictive value of apparent diffusion coefficient (ADC) histogram in preoperative magnetic resonance imaging (MRI) of the 2 cm peritumoral edema zone for spatial pattern of recurrence in IDH wild-type glioblastoma (GB). Methods
ZENG Linlan, WANG Hanwei, TIAN Jing, RAN Qisheng, ZHANG Letian   +4 more
doaj   +1 more source

A method of generating multivariate non-normal random numbers with desired multivariate skewness and kurtosis [PDF]

Behavior Research Methods, 2019
In social and behavioral sciences, data are typically not normally distributed, which can invalidate hypothesis testing and lead to unreliable results when being analyzed by methods developed for normal data. The existing methods of generating multivariate non-normal data typically create data according to specific univariate marginal measures such as ...
Wen, Qu, Haiyan, Liu, Zhiyong, Zhang
openaire   +2 more sources