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Artificial Intelligence-Based Prediction of Subjective Refraction and Clinical Determinants of Prediction Error. [PDF]

Diagnostics (Basel)
Candan O, Saglam I, Orman G, Unlu N, Burcu A, Candan Y.   +5 more
europepmc   +1 more source

Assessment of Objective Cyclotorsion Changes Following Symmetric and Asymmetric Graded Recession of Inferior Oblique Muscle for V-Pattern Strabismus. [PDF]

Clin Ophthalmol
Ordon AJ, Simiera J, Rosa A, Loba P.
europepmc   +1 more source

Leveraging Large Language Models for Early Detection of Anomaly Work Injury Cases: Data-Driven Approach to Rehabilitation Efficiency. [PDF]

JMIR Rehabil Assist Technol
Chen PQ, Gu HYW, Lo HKY, Chang WC, Lai CJM, Lai SHS, Cheng ASK, Ng PHF.   +7 more
europepmc   +1 more source

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Confidence Intervals for Mean Absolute Deviations

The American Statistician, 2003
The mean absolute deviation is a simple and informative measure of variability. Approximate confidence intervals for mean absolute deviations in one-group and two-group designs are derived and are shown to have excellent small-sample properties under moderate nonnormality. Sample size planning formulas are derived.
Bonett, Douglas G., Seier, Edith
openaire   +3 more sources

A note on mean absolute deviation

Information Sciences, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yager, Ronald R., Alajlan, Naif
openaire   +1 more source

Computing mean absolute deviation under uncertainty

Applied Soft Computing, 2010
In this paper the problem of computing the mean absolute deviation in a set of uncertain variables is discussed. The uncertainty is modeled by closed intervals and fuzzy intervals. Some polynomial algorithms for determining the lower and upper bounds for the mean absolute deviation under interval uncertainty are proposed.
Barbara Gładysz, Adam Kasperski
openaire   +1 more source