Results 261 to 270 of about 7,757,311 (314)

IV Thrombolysis Facilitates Interventional Reperfusion in Non‐Cardioembolic but Not Cardioembolic Stroke

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Intravenous thrombolysis (IVT) before thrombectomy for ischemic stroke may alter clot structure and procedural performance. We investigated how IVT relates to thrombectomy metrics across stroke etiologies. Methods We performed a time‐to‐event analysis of consecutive patients with anterior circulation large vessel occlusion (acLVO ...
Annahita Sedghi, Daniel P. O. Kaiser, Martin Arndt, Norma J. Diel, Erik Simon, Witold H. Polanski, Volker Puetz, Hagen B. Huttner, Timo Siepmann   +8 more
wiley   +1 more source

Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network. [PDF]

Sci Rep
Yang Y, Hu R, Wang W, Zhang T.
europepmc   +1 more source

Development of a quantitative prediction algorithm for human cord blood-derived CD34⁺ hematopoietic stem-progenitor cells using parametric and non-parametric machine learning models. [PDF]

Sci Rep
Leung CK, Zhu P, Loke I, Tang KF, Leung HC, Yeung CF.   +5 more
europepmc   +1 more source

Discovering the ecological structure of different macrophyte groups in rivers using non-parametric and parametric multivariate ordination techniques. [PDF]

Sci Rep
Budka A, Szoszkiewicz K, Pietruczuk K, Agaj T.   +3 more
europepmc   +1 more source

A novel non-parametric statistical method in reliability theory: Mathematical characterization and analysis of asymmetric data in the fields of biological sciences and engineering. [PDF]

Heliyon
Al-Essa LA, Etman WBH, Eliwa MS, El-Morshedy M, El-Sagheer RM.   +4 more
europepmc   +1 more source

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A Non-parametric Fisher Kernel

2021
In this manuscript, we derive a non-parametric version of the Fisher kernel. We obtain this original result from the Non-negative Matrix Factorization with the Kullback-Leibler divergence. By imposing suitable normalization conditions on the obtained factorization, it can be assimilated to a mixture of densities, with no assumptions on the distribution
Pau Figuera, Pablo García Bringas
openaire   +1 more source

Non-Parametric Regression Methods

Computational Management Science, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Non-Parametric Analysis of Covariance

Biometrics, 1995
An analysis of covariance model where the covariate effect is assumed only to be smooth is considered. The possibility of different shapes of covariate effect in different groups is also allowed and tests of equality and of parallelism across groups are constructed.
Young, Stuart G., Bowman, Adrian W.
openaire   +2 more sources

Non-parametric estimates of overlap

Statistics in Medicine, 2001
Kernel densities provide accurate non-parametric estimates of the overlapping coefficient or the proportion of similar responses (PSR) in two populations. Non-parametric estimates avoid strong assumptions on the shape of the populations, such as normality or equal variance, and possess sampling variation approaching that of parametric estimates.
R A, Stine, J F, Heyse
openaire   +2 more sources