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Fall 2014
Geostatistical modeling in presence of extreme values needs special attention. Certain extreme high values known as outliers require proper treatment or mineral resources may be overstated. A number of methodologies are proposed in this thesis to identify and manage outliers. The main goal of the...
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Fall 2024
Geostatistical models are often generated with widely spaced data configurations. Data collection costs prohibit exhaustive sampling and necessitate statistical inference from limited samples. Spatial prediction with sparse data in the presence of extreme values is an enduring challenge in the...
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Parsimonious Contaminated Shifted Asymmetric Laplace Mixtures: Unsupervised Learning with Outlier Identification for Asymmetric Clusters in High Dimensions
DownloadFall 2021
A family of parsimonious contaminated shifted asymmetric Laplace mixtures is developed for asymmetric clusters in the presence of outliers and noise (referred to as bad points herein). A series of constraints are applied to a modified factor analyzer structure of the scale matrix parameters,...
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Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization
DownloadFall 2015
Availability of large amounts of industrial process data is allowing researchers to explore new data-based modelling methods. In this thesis, Gaussian process (GP) regression, a relatively new Bayesian approach to non-parametric data based modelling is investigated in detail. One of the primary...
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Fall 2020
Data-driven modeling approaches have been widely studied and applied to the process industries for inferential sensor development, process monitoring and fault detection and early warnings, etc. Essential information of process, like dynamic and relationships between process variables are buried...