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Nedret Billor
Researcher at Auburn University
Publications - 43
Citations - 1275
Nedret Billor is an academic researcher from Auburn University. The author has contributed to research in topics: Outlier & Robust statistics. The author has an hindex of 16, co-authored 41 publications receiving 1096 citations. Previous affiliations of Nedret Billor include University of Sheffield.
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A Distribution-Free Multivariate Phase I Location Control Chart for Subgrouped Data from Elliptical Distributions
TL;DR: In this paper, the authors developed a Phase I location control chart for multivariate elliptical processes, which can then be used to estimate the parameters for phase II monitoring using Monte Carlo simulation.
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A Re-Weighted Least Squares Method for Robust Regression Estimation
TL;DR: In this paper, instead of using the diagonal elements of the projection matrix as a measure of leverage, the robust distance proposed by Hadi (1992a, 1994) was used to eliminate the distortion effect of masking by constructing a measure for the observed points which is free from the effects of multivariate outliers and clustering in the X-space.
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Recognition of Western style musical genres using machine learning techniques
Mohamed M. Mostafa,Nedret Billor +1 more
TL;DR: The results show that machine learning models outperform traditional statistical techniques in classifying and clustering different music genres due to their robustness and flexibility of modeling algorithms.
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Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis.
TL;DR: This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock.
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Validation of prediction equations for apparent metabolizable energy of corn distillers dried grains with solubles in broiler chicks
TL;DR: Results indicated that validation is necessary to quantify the expected error associated with practical application of each individual prediction equation to external data as well as consistent with the expectation of predictive performance based on internal measures of fit for each equation.