Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation

Jung, Kwanghee; Lee, Jae‐Hoon; Gupta, Vibhuti; Cho, Gyeongcheol

doi:10.3389/fpsyg.2019.02215

Cited by 86 publications

(91 citation statements)

References 23 publications

(30 reference statements)

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“…The amplitude of the confidence interval in Figure 3 reflects the precision of the estimates for the maximum notes mode, given the GEV distribution. Other confidence intervals via bootstrap could be considered, such as bootstrap-t and BCa [37,38]. We emphasize that the strategy adopted is innovative in the context of sensory notes and the comparison of confidence intervals can be done as future work.…”

Section: Resultsmentioning

confidence: 99%

Intensive Computational Method Applied for Assessing Specialty Coffees by Trained and Untrained Consumers

Liska¹,

Beijo²,

Cirillo³

et al. 2021

Recent Advances in Numerical Simulations

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The sensory analysis of coffees assumes that a sensory panel is formed by tasters trained according to the recommendations of the American Specialty Coffee Association. However, the choice that routinely determines the preference of a coffee is made through experimentation with consumers, in which, for the most part, they have no specific ability in relation to sensory characteristics. Considering that untrained consumers or those with basic knowledge regarding the quality of specialty coffees have little ability to discriminate between different sensory attributes, it is reasonable to admit the highest score given by a taster. Given this fact, probabilistic studies considering appropriate probability distributions are necessary. To access the uncertainty inherent in the notes given by the tasters, resampling methods such as Monte Carlo’s can be considered and when there is no knowledge about the distribution of a given statistic, p-Bootstrap confidence intervals become a viable alternative. This text will bring considerations about the use of the non-parametric resampling method by Bootstrap with application in sensory analysis, using probability distributions related to the maximum scores of tasters and accessing the most frequent region (mode) through computational resampling methods.

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Section: Resultsmentioning

confidence: 99%

Intensive Computational Method Applied for Assessing Specialty Coffees by Trained and Untrained Consumers

Liska¹,

Beijo²,

Cirillo³

et al. 2021

Recent Advances in Numerical Simulations

View full text Add to dashboard Cite

show abstract

“…The 95% Con dence Intervals (CI) of the above-speci ed parameters were also calculated using a percentile bootstrap method based on 10 k samples [23]. The percentile method was chosen for its conservative nature, as it tends to produce wider CI less sensitive to population value and sample size [24]. To compare the maximum errors made between the Test-Retest trials and the Human-Software evaluations, we used a twotailed two-sample t-test [25], exploring the signi cant differences between measures.…”

Section: Discussionmentioning

confidence: 99%

Categorisation of Sit-To-Stand Motion Pattern. Human vs Automated Quantitative Assessments.

Job

Battista

Stanzani

et al. 2020

Preprint

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BACKGROUND: The Sit-to-Stand (STS) test is widely used in clinical practice as an indicator of lower-limb functionality decline, especially for older adults. Hitherto, due to its high variability, there is no standard approach for categorising the STS motion pattern, and the vision-based evaluation remains the most reliable method to evaluate people’ performance. This paper presents a comparative analysis between visual assessments and an automated-software approach for the categorisation of STS, relying on registrations from a force plate. METHODS: A group of 5 participants (30 ± 6 years) took part in 2 different sessions of visual inspections on 200 STS movements randomly extracted from a dataset of 742 acquisitions under self-paced and controlled speed conditions. Assessors were asked to identify three specific STS events from the Ground Reaction Force, simultaneously with the software analysis: the start of the trunk movement (Initiation), the beginning of the stable upright stance (Standing) and the sitting movement (Sitting). The Test-Retest Reliability between first and second visual evaluations was compared with the Inter-Rater Agreement between visual and software assessments, as indexes of human and software performance, respectively.RESULTS: No statistical differences between methods were found for the identification of the Initiation and the Sitting events at self-paced speed and for only the Sitting event at controlled speed. The estimated significant values of maximum discrepancy between visual and automated assessments were 0.200 s [0,039; 0.361] in unconstrained conditions and 0,340 s [0,014; 0,666] for standardised movements.CONCLUSIONS: The software assessments displayed an overall good agreement against visual evaluations of the Ground Reaction Force, relying, at the same time, on objective measures. In this sense, the proposed approach can provide robust and consistent data in the field of Big Data analytics, augmenting the performance of artificial intelligence methods for Human Activity Recognition tasks.

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“…Generalized structured component analysis (GSCA) [56][57][58] was conducted to evaluate the proposed research model. GSCA is an approach to component-based structural equation modeling and works well with a small sample size without requiring rigid distributional assumptions such as multivariate normality [59][60][61]. Web-based software for GSCA was used for the analysis, available online at [62].…”

Section: Perceived Visual Designmentioning

confidence: 99%

BlocklyXR: An Interactive Extended Reality Toolkit for Digital Storytelling

Jung

Nguyen²,

Lee

2021

Applied Sciences

Self Cite

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Traditional in-app virtual reality (VR)/augmented reality (AR) applications pose a challenge of reaching users due to their dependency on operating systems (Android, iOS). Besides, it is difficult for general users to create their own VR/AR applications and foster their creative ideas without advanced programming skills. This paper addresses these issues by proposing an interactive extended reality toolkit, named BlocklyXR. The objective of this research is to provide general users with a visual programming environment to build an extended reality application for digital storytelling. The contextual design was generated from real-world map data retrieved from Mapbox GL. ThreeJS was used for setting up, rendering 3D environments, and controlling animations. A block-based programming approach was adapted to let users design their own story. The capability of BlocklyXR was illustrated with a use case where users were able to replicate the existing PalmitoAR utilizing the block-based authoring toolkit with fewer efforts in programming. The technology acceptance model was used to evaluate the adoption and use of the interactive extended reality toolkit. The findings showed that visual design and task technology fit had significantly positive effects on user motivation factors (perceived ease of use and perceived usefulness). In turn, perceived usefulness had statistically significant and positive effects on intention to use, while there was no significant impact of perceived ease of use on intention to use. Study implications and future research directions are discussed.

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Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation

Cited by 86 publications

References 23 publications

Intensive Computational Method Applied for Assessing Specialty Coffees by Trained and Untrained Consumers

Intensive Computational Method Applied for Assessing Specialty Coffees by Trained and Untrained Consumers

Categorisation of Sit-To-Stand Motion Pattern. Human vs Automated Quantitative Assessments.

BlocklyXR: An Interactive Extended Reality Toolkit for Digital Storytelling

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