Robust recursive estimation of GARCH models

Cipra, Tomáš; Hendrych, Radek

doi:10.14736/kyb-2018-6-1138

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…In the areas of orbit computation, military surveillance and navigational the Kalman filter has found widespread usage due to its advantages of real-time, quick, efficient and strong anti-interference. Also, it has a significant impact in the areas of digital image processing as well as other study areas like machine learning [69].…”

Section: Drone Swarm Tracking Pipelinementioning

confidence: 99%

Towards Reliable Identification and Tracking of Drones Within a Swarm

Kumari,

Lee,

Barca

et al. 2024

J Intell Robot Syst

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Drone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm.

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Section: Drone Swarm Tracking Pipelinementioning

confidence: 99%

Towards Reliable Identification and Tracking of Drones Within a Swarm

Kumari,

Lee,

Barca

et al. 2024

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…Another statistical models can directly apply to address missing values in EEG time series, such as autoregression (AR) and multivariate autoregression (MAR), maximum likelihoodbased methods or Bayesian network. However, these works rarely exploit the temporal smoothness and correlations among the observations effectively [33][34].…”

Section: A Missing Data Imputationmentioning

confidence: 99%

iTa-DFiE: An Innovative Tensor Algebra-Based Detection Framework for Incomplete Noninvasive Electroencephalography

Anh Thi Nguyen,

Tao,

Yang

et al. 2024

IEEE Access

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The paper presents a novel recognition framework for incomplete noninvasive Electroencephalography (EEG) signals relying on the recent advances in tensor algebra, named as An Innovative Tensor Algebra -based Detection Framework for Incomplete Noninvasive Electroencephalogra-phy (iTa-DFiE). iTa-DFiE is motivated to improve the diagnostic performance by tackling the major problems shared by a variety of noninvasive EEG-based Brain-Computer Interfaces (BCIs) application is tensorial structured time series with occlusions. The aforementioned challenge setting is solved on two major thrusts, including:(1) tensor completion: discovering hidden patterns and learning their evolving trends to offer missing values imputation via improvement of standard Kalman Filter approach; (2) tensor decomposition: extracting essential hidden information from multiaspect data tensor via extending the most well-known tensor factorization Tucker. The effectiveness and efficientness of the proposed tensor-based framework is proved via successfully improving the pattern classification results on two real-world noninvasive EEG-based motor imagery BCI with diverse corrupted data scenarios, especially in occurrence of consecutive missing observations. Strikingly, iTa-DFiE also outperforms the conventional matrices-based methods and the state-of-the-art tensor techniques in terms of missing reconstructions and feature extraction as well.

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“…The authors of Gerencsér et al (2010) show convergence analysis of recursive QML estimation for GARCH processes based on BMP-theory with the use of a resetting mechanism. A selfweighted recursive estimation algorithm for GARCH models was proposed by Cipra and Hendrych (2018) with a robustification in Hendrych and Cipra (2018). However, none of the above references mention convexity nor estimation issues of small ω parameter values for GARCH models.…”

Section: Introductionmentioning

confidence: 99%

AdaVol: An Adaptive Recursive Volatility Prediction Method

Werge¹,

Wintenberger²

2020

Preprint

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The Quasi-Maximum Likelihood (QML) procedure is widely used for statistical inference due to its robustness against overdispersion. However, while there are extensive references on non-recursive QML estimation, recursive QML estimation has attracted little attention until recently. In this paper, we investigate the convergence properties of the QML procedure in a general conditionally heteroscedastic time series model, extending the classical offline optimization routines to recursive approximation. We propose an adaptive recursive estimation routine for GARCH models using the technique of Variance Targeting Estimation (VTE) to alleviate the convergence difficulties encountered in the usual QML estimation. Finally, empirical results demonstrate a favorable trade-off between the ability to adapt to time-varying estimates and stability of the estimation routine.

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Robust recursive estimation of GARCH models

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Cited by 5 publications

References 49 publications

Towards Reliable Identification and Tracking of Drones Within a Swarm

Towards Reliable Identification and Tracking of Drones Within a Swarm

iTa-DFiE: An Innovative Tensor Algebra-Based Detection Framework for Incomplete Noninvasive Electroencephalography

AdaVol: An Adaptive Recursive Volatility Prediction Method

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