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1986
Our task in writing this chapter is to outline a strategy for analyzing data that we believe to be better suited to most psychological research than the most widely used statistical techniques (e.g., t, F, and chi square tests, product moment correlation, regression, covariance, discriminant, and factor analyses).
Warren Thorngate, Barbara Carroll
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Our task in writing this chapter is to outline a strategy for analyzing data that we believe to be better suited to most psychological research than the most widely used statistical techniques (e.g., t, F, and chi square tests, product moment correlation, regression, covariance, discriminant, and factor analyses).
Warren Thorngate, Barbara Carroll
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Missing ordinal patterns in correlated noises
Physica A: Statistical Mechanics and its Applications, 2010Abstract Recent research aiming at the distinction between deterministic or stochastic behavior in observational time series has looked into the properties of the “ordinal patterns” [C. Bandt, B. Pompe, Phys. Rev. Lett. 88 (2002) 174102]. In particular, new insight has been obtained considering the emergence of the so-called “forbidden ordinal ...
Carpi, Laura C. +2 more
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Quantifying interdependence using the missing joint ordinal patterns
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019In this paper, we develop the concept of forbidden/missing ordinal patterns into the forbidden/missing joint ordinal patterns and propose the ratio of the number of missing joint ordinal patterns (RMJPs) as a sign of interdependence. RMJP in a surrogate analysis can be used to differentiate the forbidden joint ordinal patterns from the missing joint ...
Yi, Yin +4 more
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Ordinal pattern based similarity analysis for EEG recordings
Clinical Neurophysiology, 2010Ordinal patterns analysis such as permutation entropy of the EEG series has been found to usefully track brain dynamics and has been applied to detect changes in the dynamics of EEG data. In order to further investigate hidden nonlinear dynamical characteristics in EEG data for differentiating brain states, this paper proposes a novel dissimilarity ...
Gaoxiang, Ouyang +3 more
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Pipelined reconfigurable accelerator for ordinal pattern encoding
2014 IEEE 25th International Conference on Application-Specific Systems, Architectures and Processors, 2014Ordinal analysis is a statistical method for analysing the complexity of time series. This method has been used in characterising dynamic changes in time series, with various applications such as financial risk modelling and biomedical signal processing. Ordinal pattern encoding is a fundamental calculation in ordinal analysis.
Ce Guo, Wayne Luk, Stephen Weston
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Ordinal Pattern: A New Descriptor for Brain Connectivity Networks
IEEE Transactions on Medical Imaging, 2018Brain connectivity networks based on magnetic resonance imaging (MRI) or functional MRI (fMRI) data provide a straightforward way to quantify the structural or functional systems of the brain. Currently, there are several network descriptors developed for representing and analyzing brain connectivity networks.
Daoqiang Zhang +5 more
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Two-phase flow pattern transition behaviors on experimental established ordinal pattern networks
Chaos: An Interdisciplinary Journal of Nonlinear ScienceIdentifying the flow pattern transition dynamics is a fundamental challenge in modeling a two-phase flow system. In this paper, we investigate the gas–liquid two-phase flow pattern transition behaviors with analyzing the topology structures of the experimental established gas–liquid two-phase flow complex networks.
Meng Du +5 more
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Complexity from ordinal pattern positioned slopes (COPPS)
Chaos, Solitons & FractalsMeasuring complexity allows to characterize complex systems. Existing techniques are limited to simultaneously measure complexity from short length data sets, detect transitions and periodic dynamics. This paper presents an approach based on ordinal pattern positioned slopes (OPPS).
Eyebe Fouda, J. +4 more
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Robust Ear Recognition Using Gradient Ordinal Relationship Pattern
2015A reliable personal recognition based on ear biometrics is highly in demand due to its vast application in automated surveillance, law enforcement etc. In this paper a robust ear recognition system is proposed using gradient ordinal relationship pattern.
Aditya Nigam, Phalguni Gupta
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Ordinal Pattern Distributions in Dynamical Systems
Discrete Dynamics and Difference Equations, 2010K. Keller, M. Sinn
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