Dae-Heung Jang

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Outliers can distort many measures in data analysis and statistical modeling, and influential points can have disproportionate impact on the estimated values of model parameters. Jang and Anderson-Cook (2013) proposed a new set of graphical summaries, called firework plots, as simple tools for evaluating the impact of outliers and influential points in(More)
A historically common choice for evaluating response surface designs is to use alphabetic optimality criteria. Single-number criteria such as D, A, G, and V optimality do not completely reflect the estimation or prediction variance characteristics of the designs in question. For prediction-based assessment, alternatives to single-number summaries include(More)
In cluster analysis, many numerical measures to detect which data points are influential have been proposed in the past literature. These numerical measures provide only limited information about which data points are influential but fail to reveal deeper relationships between the observations. They describe an overall pattern but fail to provide details(More)
Orthogonality or near-orthogonality is an important property in the design of experiments. Supersaturated designs are natural when we wish to investigate the main effects for a large number of factors but are restricted to a small number of runs. These supersaturated designs, by definition, cannot satisfy pairwise orthogonality of all the factor columns in(More)
Outliers can distort many measures for data analysis. We propose a new set of graphical summaries, called firework plots, as simple tools for evaluating the impact of outliers in data exploration and regression assessment. One variation of the plot focuses on the impact of extreme observations on the mean and standard deviation by using curves that trace(More)
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