Confidence Interval for Locations of Non-kurtosis and Large Kurtosis Leptokurtic Symmetric Distributions

Al-Athari, Faris M.

doi:10.3923/jas.2011.528.534

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…. , n. The MAD was first advanced as a robust estimate of scale by Hampel (1974), and has since been used in a variety of statistical applications, including: outlier detection and labeling (Iglewicz and Hoaglin, 1993;Malinowski, 2009;Sheskin, 2000;Sprent and Smeeton, 2000); preprocessing highthroughput gene expression microarray data (Brideau et al, 2003;Chung et al, 2008;Malo et al, 2006;Smith et al, 2003;Zhang et al, 2005); calculating limits in robust quality control charts (Abu-Shawiesh, 2008;Adekeye and Azubuike, 2012;Wu et al, 2002); and, calculating robust confidence intervals for measures of location in skewed and outlier contaminated distributions (Abu-Shawiesh et al, 2008;Al-Athari, 2011).…”

Section: Introductionmentioning

confidence: 99%

Finite-Sample Bias-Correction Factors for the Median Absolute Deviation

Hayes¹

2014

Communications in Statistics - Simulation and Computation

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Objectives: To test the goodness-of-fit of first trimester biochemistry adjusted by local population correction factor. Methods: From a previous study, free-Beta-hCG = 0.726 and PAPP-A = 0.921 correction factors were applied on risks model. Prospective biochemistry was included with periodic readjustments. One-sample t-test was calculated for all-time log10MoM adjusted distribution. Results: On 2608 sera, the overall adjusted log10MoM was-0.003 with SD of 0.2615 for free-Beta-hCG and 0.005 with 0.2329 SD for PAPP-A; neither the overall nor the characteristics-dependent distributions (table 1) were statistically different from the ideal 0.000 log10MoM. Periodic correction factors ranged 0.705 to 0.768 for free-Beta-hCG and 0.874 to 0.995 for PAPP-A. Conclusions: The adjusted biochemical model is robust, particularly for singleton spontaneous pregnancies of non-smoker women, irrespective of parity. Model stability requires adjustment.

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Section: Introductionmentioning

confidence: 99%

Finite-Sample Bias-Correction Factors for the Median Absolute Deviation

Hayes¹

2014

Communications in Statistics - Simulation and Computation

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“…The feature extraction requires the identified features of Kurtosis Value (KTV) and Katz Fractal Dimension (KFD) in the beta (12-30 Hz) frequency band. KTV provides information about the degree of concentration of the signal around the mean [14]. KFD provides information about the energy decay of a signal [15].…”

Section: Feature Extraction Enginementioning

confidence: 99%

An 8.62 μ W Processor for Autism Spectrum Disorder Classification using Shallow Neural Network

Aslam

Hafeez

Heidari

et al. 2021

2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)

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Autism Spectrum Disorder (ASD) is the most prevalent child neurological and developmental disorder causing cognitive and behavioral impairments. The early diagnosis is an urgent need for the treatment and rehabilitation of ASD patients. This work presents electroencephalogram (EEG) based ASD classification processor implementation that targets a patch-form factor sensor for long time monitoring in a wearable device. A patient is classified as ASD or typically developing using scalp EEG. The selection of frontal and parietal lobe electrodes causes minimum uneasiness to the children. The proposed and implemented algorithm utilizes only four EEG electrodes. The processor is implemented and validated on Artix-7 FPGA, requiring only 26229 lookup tables and 15180 flip flops. The hardware efficient implementation of the complex kurtosis value and Katz fractal dimension features using kurtosis value indicator and Katz fractal dimension indicator with 54% and 38% efficient implementations, respectively, is provided. A hardware feasible shallow neural network architecture is used for the ASD classification. The implemented system classifies the ASD with a high classification accuracy of 85.5% using the power and latency of 8.62 µW and 2.25 milliseconds, respectively.feeling of disgrace and repeated visits to neurologists. Fig. 1 shows the difference between the conventional ADOS-2 diagnosis and the proposed solution to solve the pain problem. The proposed solution (longer-term) would be able to diagnose a child as ASD or TD earlier in the form of a wearable head-band system on chip (SoC) processor. The processor would pre-process the brain waves (EEG), extract suitable features, and classify the child as ASD or TD using suitable machine learning (ML) or deep neural network (DNN) classification method with limited electrodes. It would not only avoid the stigma associated with the prolonged diagnosis but also reduce the rehabilitation costs due to early intervention [4].

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Approximating median absolute deviation with bounded error

Chen

Song

Wei³

et al. 2021

Proc. VLDB Endow.

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The median absolute deviation (MAD) is a statistic measuring the variability of a set of quantitative elements. It is known to be more robust to outliers than the standard deviation (SD), and thereby widely used in outlier detection. Computing the exact MAD however is costly, e.g., by calling an algorithm of finding median twice, with space cost O ( n ) over n elements in a set. In this paper, we propose the first fully mergeable approximate MAD algorithm, OP-MAD, with one-pass scan of the data. Remarkably, by calling the proposed algorithm at most twice, namely TP-MAD, it guarantees to return an (ϵ, 1)-accurate MAD, i.e., the error relative to the exact MAD is bounded by the desired ϵ or 1. The space complexity is reduced to O ( m ) while the time complexity is O ( n + m log m ), where m is the size of the sketch used to compress data, related to the desired error bound ϵ. To get a more accurate MAD, i.e., with smaller ϵ, the sketch size m will be larger, a trade-off between effectiveness and efficiency. In practice, we often have the sketch size m ≪ n , leading to constant space cost O (1) and linear time cost O ( n ). The extensive experiments over various datasets demonstrate the superiority of our solution, e.g., 160000× less memory and 18x faster than the aforesaid exact method in datasets pareto and norm . Finally, we further implement and evaluate the parallelizable TP-MAD in Apache Spark, and the fully mergeable OP-MAD in Structured Streaming.

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Confidence Interval for Locations of Non-kurtosis and Large Kurtosis Leptokurtic Symmetric Distributions

Cited by 4 publications

References 8 publications

Finite-Sample Bias-Correction Factors for the Median Absolute Deviation

Finite-Sample Bias-Correction Factors for the Median Absolute Deviation

An 8.62 μ W Processor for Autism Spectrum Disorder Classification using Shallow Neural Network

Approximating median absolute deviation with bounded error

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