Results 11 to 20 of about 963,417 (295)
An Adversarial Optimization Approach to Efficient Outlier Removal [PDF]
Journal of Mathematical Imaging and Vision, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jin Yu 0001 +3 more
core +16 more sources
Heteroscedasticity testing after outlier removal [PDF]
Econometric Reviews, 2020 Given the effect that outliers can have on regression and specification testing, a vastly used robustification strategy by practitioners consists in: (i) starting the empirical analysis with an outlier detection procedure to deselect atypical data values; then (ii) continuing the analysis with the selected non-outlying observations.
Berenguer-Rico, V, Wilms, I
openaire +3 more sources
Valid Inference Corrected for Outlier Removal [PDF]
Journal of Computational and Graphical Statistics, 2019 21 pages, 6 figures, 2 ...
Shuxiao Chen, Jacob Bien
openaire +3 more sources
STAR_outliers: a python package that separates univariate outliers from non-normal distributions
BioData Mining, 2023 There are not currently any univariate outlier detection algorithms that transform and model arbitrarily shaped distributions to remove univariate outliers.
John T. Gregg, Jason H. Moore
doaj +1 more source
Nearest Centroid Classifier with Outlier Removal for Classification
JITeCS (Journal of Information Technology and Computer Science), 2020 Classification method is misled by outlier. However, there are few research of classification with outlier removal, especially for Nearest Centroid Classifier Method. The proposed methodology consists of two stages.
Aditya Hari Bawono +2 more
doaj +1 more source
Journal of Medical Internet Research, 2023 BackgroundReference intervals (RIs) play an important role in clinical decision-making. However, due to the time, labor, and financial costs involved in establishing RIs using direct means, the use of indirect methods, based on ...
Dan Yang +10 more
doaj +1 more source
IEEE Access, 2022 The software engineering researchers have worked on different dimensions to facilitate better software effort estimates, including those focusing on dataset quality improvement.
Swarnima Singh Gautam, Vrijendra Singh
doaj +1 more source
An adaptive outlier removal aided k-means clustering algorithm
Journal of King Saud University: Computer and Information Sciences, 2022 K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy. Improve the clustering accuracy of
Nawaf H.M.M. Shrifan +2 more
doaj +1 more source
Outliers May Not Be Automatically Removed
Journal of Experimental Psychology: General, 2022 Researchers often remove outliers when comparing groups. It is well documented that the common practice of removing outliers within groups leads to inflated type I error rates. However, it was recently argued by André that if outliers are instead removed across groups, type I error rates are not inflated. The same study discusses that removing outliers
openaire +3 more sources
SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data.
A. A. Matkan +4 more
doaj +1 more source