The Performance of Redescending M-Estimators when Outliers are in Two Dimensional Space

Ebele, Anekwe Stella; Iheanyi, Onyeagu Sidney

doi:10.34198/ejms.8222.295304

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The authors of [9] carried out a statistical comparison for outlier detection using synthetic bases including 10% of modified data. They conducted the Hampel's test [25] and they found that the Quartile method [26] was more efficient for finding outliers, but the both of them are not suitable for big data . The success of such diagnostic devices used in remote regions by untrained healthcare users depends on the development of advanced image and signal processing techniques that make these devices noise-tolerant and provide accurate diagnos-tics without requiring a high-quality infrastructure [10].…”

Section: Introductionmentioning

confidence: 99%

Smart K Nearest Neighbor Outlier Detection for Electroencephalogram signal

Abid,

khediri,

Thajaoui

et al. 2023

Preprint

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Electroencephalogram (EEG) data suffer from artifacts such as poor imagination or loss of concen-tration during the recognition process. The negative impact of artifacts on the quality of informationand EEG signal analysis reduces the Quality of Service "QoS" and Quality of Information "QoI" ine-health applications. This negative impact can be avoided by identifying outliers with anomaly detec-tion algorithms. Aberrant values’ identification using Euclidean distance for the K Nearest Neighbor(KNN) process between the recorded EEG values separates the time series data recorded by a personinto neural activity and artifact. The algorithms using KNN often require knowledge of the data char-acteristics to configure one or more parameters (such as the number of neighbors K and distance D). However, our proposed solution does not require the initial setting of KNN process or any additional parameters. We propose a Smart KNN Outlier Detector (SKOD) which is an unsupervised non-parametric algo-rithm. We evaluated SKOD using various combinations of real EEG data of 140 trials with three channels of the benchmark Brain-Computer Interface "BCI competition II". We tested the perfor-mance of our solution on EEG data as provided by the studied patient (the subject) with the inclusionof different numbers of outliers. Our proposed detector achieves more than 60% of sensitivity andspecificity for detecting abnormal values with outlier detection close to 100%.

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Section: Introductionmentioning

confidence: 99%

Smart K Nearest Neighbor Outlier Detection for Electroencephalogram signal

Abid,

khediri,

Thajaoui

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Improved regression in ratio type estimators based on robust M-estimation

Rather¹,

Koçyiğit

Onyango

et al. 2022

PLoS ONE

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In this article, a new robust ratio type estimator using the Uk’s redescending M-estimator is proposed for the estimation of the finite population mean in the simple random sampling (SRS) when there are outliers in the dataset. The mean square error (MSE) equation of the proposed estimator is obtained using the first order of approximation and it has been compared with the traditional ratio-type estimators in the literature, robust regression estimators, and other existing redescending M-estimators. A real-life data and simulation study are used to justify the efficiency of the proposed estimators. It has been shown that the proposed estimator is more efficient than other estimators in the literature on both simulation and real data studies.

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The Performance of Redescending M-Estimators when Outliers are in Two Dimensional Space

Cited by 2 publications

References 8 publications

Smart K Nearest Neighbor Outlier Detection for Electroencephalogram signal

Smart K Nearest Neighbor Outlier Detection for Electroencephalogram signal

Improved regression in ratio type estimators based on robust M-estimation

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