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Relevant bibliographies by topics / Source Non-Stationarity (SNS)

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Journal articles
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Academic literature on the topic 'Source Non-Stationarity (SNS)'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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Journal articles on the topic "Source Non-Stationarity (SNS)"

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Moroke, Ntebogang Dinah. "An Optimal Generalized Autoregressive Conditional Heteroscedasticity Model for Forecasting the South African Inflation Volatility." Journal of Economics and Behavioral Studies 7, no. 4(J) (2015): 134–49. http://dx.doi.org/10.22610/jebs.v7i4(j).600.

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Abstract: In most cases, financial variables are explained by leptokurtic distribution and often fail the assumption of normal distribution. This paper sought to explore the robustness of GARCHâ€“type models in forecasting inflation volatility using quarterly time series data spanning 2002 to 2014. The data was sourced from the South African Reserve Bank database. SAS version 9.3 was used to generate the results. The initial analyses of data confirmed non-linearity, hereroscedasticity and non-stationarity in the series. Differencing was imposed in a log transformed series to induce stationarit

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Wang, Ziwei, Wen Zhang, Siyang Li, Xinru Chen, and Dongrui Wu. "Unsupervised domain adaptation for cross-patient seizure classification." Journal of Neural Engineering, October 31, 2023. http://dx.doi.org/10.1088/1741-2552/ad0859.

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Abstract Objective. Epileptic seizure is a chronic neurological disease affecting millions of patients. Electroencephalogram (EEG) is the gold standard in epileptic seizure classification. However, its low signal-to-noise ratio, strong non-stationarity, and large individual difference nature make it difficult to directly extend the seizure classification model from one patient to another. This paper considers multi-source unsupervised domain adaptation for cross-patient EEG-based seizure classification, i.e., there are multiple source patients with labeled EEG data, which are used to label the

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He, Roxanne, Malwina Luczak, and Nathan Ross. "Cutoff for the logistic SIS epidemic model with self-infection." Advances in Applied Probability, June 4, 2025, 1–28. https://doi.org/10.1017/apr.2025.16.

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We study a variant of the classical Markovian logistic SIS epidemic model on a complete graph, which has the additional feature that healthy individuals can become infected without contacting an infected member of the population. This additional ‘self-infection’ is used to model situations where there is an unknown source of infection or an external disease reservoir, such as an animal carrier population. In contrast to the classical logistic SIS epidemic model, the version with self-infection has a non-degenerate stationary distribution, and we derive precise asymptotics for the time to conve

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Ehrlich, Justin, Christopher Boudreaux, James Boudreau, and Shane Sanders. "An Analysis of an Alternative Pythagorean Expected Win Percentage Model." Maths and Sports 1, no. 1 (2021). https://doi.org/10.5149/ms.1229.

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Abstract Background. Contests are games in which the players compete for a prize and exert effort to increase their probability of winning. For sport contests, analysts often use the Pythagorean expected win percentage model to estimate teams’ expected wins (quality). We ask if there are alternative contest models that minimize error or information loss from misspecification and outperform the Pythagorean model. Aim. This article aims to use simulated data to select the optimal expected win percentage model among the choice of relevant alternatives. The choices include the traditional Pythagor

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Dissertations / Theses on the topic "Source Non-Stationarity (SNS)"

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Pelluri, Sai Gunaranjan. "Joint Spectro-Temporal Analysis of Moving Acoustic Sources." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4279.

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Signals generated by fast moving acoustic sources are both challenging for analysis as well as rich in information. The motion is conceptually relative between the source and receiver i.e., either one of them is moving or both are moving. Thus, the receiver would gather information about the relative motion as well as the nature of source itself. For example, direction, velocity, acceleration, number of different sources, friend/foe, etc. are all information that can be gathered. All these parameters are inherently embedded in the received signal. Given the rich information content inherent in

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