Robert St. Amant and Paul R. Cohen
Statistical operations are often facilitated by other operations. We can facilitate modeling operations by testing their input for irregularities and removing problems wherever possible. A planning representation is well-suited to this task. We describe the representation used in Igor, a system for exploratory data analysis, and its integration with two modeling systems, Pearl’s IC and Cohen’s FBD. We show that introducing outliers into the inputs of the algorithms can influence their performance. We demonstrate that a planning representation offers a flexible way of integrating outlier detection and removal into the modeling process, taking account of specific characteristics of the modeling operations involved.