Robust Bivariate Error Detection in Skewed Data with Application to Historical Radiosonde Winds

• Activity Number: 674
• Type: Topic Contributed
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Amanda Hering* and Ying Sun and Joshua Browning
• Companies: Colorado School of Mines and King Abdullah University of Science and Technology and Colorado School of Mines
• Keywords: Outliers ; Radiosonde Winds ; Skewed Distributions

Abstract

Quality control methods for observations for which more than one variable is recorded are generally based on using robust estimates of parameters for a particular distribution, and that particular distribution is usually the multivariate normal (MVN). However, many multivariate data generating processes do not produce elliptical contours, and in such cases, error detection using the MVN distribution would lead to many legitimate observations being erroneously flagged. In this work, we propose non-parametric and parametric methods for identifying errors in skewed bivariate data. In the first method, we remove potential outliers by assigning each bivariate observation a depth score and remove those observations that fall beyond a given threshold. In the second method, we first develop robust estimators for the parameters in a bivariate skew-t (BST) distribution, and these parameters are used in either a distance-based or contour-based approach to flag observations as potential outliers. We test the performance of these methods in simulation against a more common MVN outlier detection method and apply them to radiosonde launches of the wind vector for 8 pressure levels.