Results 201 to 210 of about 158,629 (258)

Spiritual Coping and Duration Since Diagnosis Associated With Quality of Life Among Women With HIV: A Cross-Sectional Study. [PDF]

AIDS Res Treat
Muchsin R, Yunitasari E, Bakar A, Hati Y.   +3 more
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Data augmentation for ordinal data

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Ordinal Clinical Outcome Modeling with Temporal Validation to Support Hospital Capacity Planning During Acute Infectious Disease Burden. [PDF]

Int J Environ Res Public Health
Sodnomdavaa T, Gantumur U.
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Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning. [PDF]

PLoS One
Lee EJ, Kim S, Lee HJ, Jhong JH.
europepmc   +1 more source

Multiscale Ordinal-Pattern Dynamics and Temporal Symmetries in a Photonic Neuron with Single and Dual Delayed Feedback. [PDF]

Entropy (Basel)
Feiveson J, Yearian M, Jones M, Aragoneses A.   +3 more
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Iron-Handling, Lipid-Oxygenation, and Hypoxia-Response Gene Expression in the Renal Cortex of Cats with Chronic Kidney Disease: An Analysis-Plan-Guided Secondary Analysis. [PDF]

Vet Sci
de Souza C.
europepmc   +1 more source

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Simulating Ordinal Data

Multivariate Behavioral Research, 2012
The increasing use of ordinal variables in different fields has led to the introduction of new statistical methods for their analysis. The performance of these methods needs to be investigated under a number of experimental conditions. Procedures to simulate from ordinal variables are then required.
P.A. Ferrari, A. Barbiero
openaire   +2 more sources

Metric Learning for Ordinal Data

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
A large amount of ordinal-valued data exist in many domains, including medical and health science, social science, economics, political science, etc. Unlike image and speech datasets of real-valued data, learning with ordinal variables (i.e., features) presents unique challenges.
Yuan Shi, Wenzhe Li, Fei Sha
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Answering Ordinal Questions with Ordinal Data Using Ordinal Statistics

Multivariate Behavioral Research, 1996
It is argued that ordinal statistical methods are often more appropriate than their more common counterparts for three types of reasons: Conclusions from them will be unaffected by monotonic transformation of the variables, they are statistically more robust when used appropriately, and they often correspond more closely to the goals of the ...
openaire   +2 more sources