Outlier detection and variable selection via difference based regression model and penalized regression

Simultaneous outlier detection and variable selection via difference-based regression model and stochastic search variable selection

“…In this section, we explain how to determine outlier candidates and set up a regression model that includes outlier candidates. We then introduce the difference-based regression model (Choi et al, 2018;Park and Kim, 2018b) in Section 2.2.…”

“…The performances of our proposed procedure are evaluated in two parts: outlier detection and variable selection. In the first part, we use three criteria proposed by Choi et al (2018) to detect outliers. Let n O be the number of true outliers, n D be the number of detected outliers, n CD be the number of correctly detected outliers, n ID be the number of incorrectly detected outliers, n IU be the number of incorrectly undetected outliers, and n CU be the number of correctly undetected non-outliers (Table 2).…”

“…In the second part, to select variables, we use two criteria (Choi, et al, 2018). Let CS be the relative frequency of correct selection, the AN be the average number of selected variables.…”

“…We use the mean-shift outlier model for outlier candidates. To determine these outlier candidates, we use the properties of an intercept estimator in the difference-based regression model (DBRM) (Choi et al, 2018;Park and Kim, 2018b). This type of model was originally used in a time series analysis to remove trends in the mean function (Park and Kim, 2018a;Park, 2018;Park et al, 2012).…”