Detecting powder mixture inhomogeneity for non-normal measurement errors

Walker, Jennifer J.; Rollins, Derrick K.

doi:10.1016/s0032-5910(97)03208-7

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…Most of the emotion categories were non-normally distributed with some left- and some right-skewed according to the histograms, and transformations did not normalise the data. Due to the robustness to normality violations [ 66 , 67 ], repeated measures ANOVA was conducted. A 3 (intensities) x 9 (emotions) repeated measures ANOVA with Greenhouse-Geisser adjustment of degrees of freedom was applied due to violation of Sphericity.…”

Section: Studymentioning

confidence: 99%

Validation of the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV): A Set of Videos Expressing Low, Intermediate, and High Intensity Emotions

2016

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Most of the existing sets of facial expressions of emotion contain static photographs. While increasing demand for stimuli with enhanced ecological validity in facial emotion recognition research has led to the development of video stimuli, these typically involve full-blown (apex) expressions. However, variations of intensity in emotional facial expressions occur in real life social interactions, with low intensity expressions of emotions frequently occurring. The current study therefore developed and validated a set of video stimuli portraying three levels of intensity of emotional expressions, from low to high intensity. The videos were adapted from the Amsterdam Dynamic Facial Expression Set (ADFES) and termed the Bath Intensity Variations (ADFES-BIV). A healthy sample of 92 people recruited from the University of Bath community (41 male, 51 female) completed a facial emotion recognition task including expressions of 6 basic emotions (anger, happiness, disgust, fear, surprise, sadness) and 3 complex emotions (contempt, embarrassment, pride) that were expressed at three different intensities of expression and neutral. Accuracy scores (raw and unbiased (Hu) hit rates) were calculated, as well as response times. Accuracy rates above chance level of responding were found for all emotion categories, producing an overall raw hit rate of 69% for the ADFES-BIV. The three intensity levels were validated as distinct categories, with higher accuracies and faster responses to high intensity expressions than intermediate intensity expressions, which had higher accuracies and faster responses than low intensity expressions. To further validate the intensities, a second study with standardised display times was conducted replicating this pattern. The ADFES-BIV has greater ecological validity than many other emotion stimulus sets and allows for versatile applications in emotion research. It can be retrieved free of charge for research purposes from the corresponding author.

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Section: Studymentioning

confidence: 99%

Validation of the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV): A Set of Videos Expressing Low, Intermediate, and High Intensity Emotions

2016

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“…It can be used when the measurement variables or the experimental errors are not normally distributed, and therefore it is applicable as a non-parametric analogue of one-way ANOVA. The Kruskal-Wallis test is widely used in life-science, but there can be found some report using it in the environmental science (Pala and Tokat, 2002;Kolpin et al, 2004), and in technical science (Walker and Rollins, 1997). In our investigation the null hypothesis was that the energy efficiency of MW method is influenced by the MW power level and the MW irradiation time.…”

Section: Energetic Analysis Of Mw Pre-treatmentsmentioning

confidence: 72%

Comparison of the effects of microwave irradiation with different intensities on the biodegradability of sludge from the dairy- and meat-industry

Beszédes

László

Horváth

et al. 2011

Bioresource Technology

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“…In the context of blood pressure Marshall (2004) discusses that the presence of a medical student results in an increase in measured blood pressure. Walker and Rollins (1997) in a robustness study of ANOVA consider a beta distribution with nonzero expectation as a possible model for measurement errors. Albers, Kallenberg and Otten (1998) in the context of screening production processes discuss situations with nonzero expectation for measurement error.…”

Section: Standard Normalmentioning

confidence: 99%

Estimation of distribution functions in measurement error models

Dattner

Reiser

2013

Journal of Statistical Planning and Inference

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Many practical problems are related to the pointwise estimation of distribution functions when data contains measurement errors. Motivation for these problems comes from diverse fields such as astronomy, reliability, quality control, public health and survey data.Recently, Dattner, Goldenshluger and Juditsky (2011) showed that an estimator based on a direct inversion formula for distribution functions has nice properties when the tail of the characteristic function of the measurement error distribution decays polynomially. In this paper we derive theoretical properties for this estimator for the case where the error distribution is smoother and study its finite sample behavior for different error distributions. Our method is data-driven in the sense that we use only known information, namely, the error distribution and the data. Application of the estimator to estimating hypertension prevalence based on real data is also examined.

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Detecting powder mixture inhomogeneity for non-normal measurement errors

Cited by 7 publications

References 3 publications

Validation of the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV): A Set of Videos Expressing Low, Intermediate, and High Intensity Emotions

Validation of the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV): A Set of Videos Expressing Low, Intermediate, and High Intensity Emotions

Comparison of the effects of microwave irradiation with different intensities on the biodegradability of sludge from the dairy- and meat-industry

Estimation of distribution functions in measurement error models

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