Browsing Theses and Dissertations - Department of Information Systems, Statistics & Management Science by Author "Barrett, Bruce E."

Browsing Theses and Dissertations - Department of Information Systems, Statistics & Management Science by Author "Barrett, Bruce E."

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  • McCracken, Amanda Kaye (University of Alabama Libraries, 2012)
    Since their invention in the 1920s, control charts have been popular tools for use in monitoring processes in fields as varied as manufacturing and healthcare. Most of these charts are designed to monitor a single process ...
  • Dovoedo, Yinaze Herve (University of Alabama Libraries, 2011)
    Tukey's traditional boxplot (Tukey, 1977) is a widely used Exploratory Data Analysis (EDA) tools often used for outlier detection with univariate data. In this dissertation, a modification of Tukey's boxplot is proposed ...
  • Oh, Dong-Yop (University of Alabama Libraries, 2012)
    Many simple and complex methods have been developed to solve the classification problem. Boosting is one of the best known techniques for improving the prediction accuracy of classification methods, but boosting is sometimes ...
  • Melnykov, Yana (University of Alabama Libraries, 2017)
    There are numerous areas of human activities where various processes are observed over time. If the conditions of the process change, it can be reflected through the shift in observed response values. The detection and ...
  • Zheng, Rong (University of Alabama Libraries, 2017)
    In general, statistical methods have two categories: parametric and nonparametric. Parametric analysis is usually made based on information regarding the probability distribution of the random variable. While, nonparametric ...
  • Zhu, Xuwen (University of Alabama Libraries, 2016)
    Cluster analysis performs unsupervised partition of heterogeneous data. It has applications in almost all fields of study. Model-based clustering is one of the most popular clustering methods these days due to its flexibility ...
  • Xu, Jie (University of Alabama Libraries, 2013)
    Ensemble models, such as bagging (Breiman, 1996), random forests (Breiman, 2001a), and boosting (Freund and Schapire, 1997), have better predictive accuracy than single classifiers. These ensembles typically consist of ...
  • Martinez Cid, Waldyn Gerardo (University of Alabama Libraries, 2012)
    Ensemble methods, such as bagging (Breiman, 1996), boosting (Freund and Schapire, 1997) and random forests (Breiman, 2001) combine a large number of classifiers through (weighted) voting to produce strong classifiers. To ...

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