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The prime objective of this paper is to make a comparative analysis between statistical, rough computing, and hybridized techniques. The comparative analysis is carried out over financial bankruptcy data set of Greek industrial bank ETEVA. It is concluded that rough computing techniques provide better accuracy 88.2% as compared to statistical techniques whereas hybridized computing techniques provides still better accuracy 94.1% as compared to rough computing techniques.<\/jats:p>","DOI":"10.4018\/ijaci.2017040103","type":"journal-article","created":{"date-parts":[[2017,3,6]],"date-time":"2017-03-06T15:52:53Z","timestamp":1488815573000},"page":"32-51","source":"Crossref","is-referenced-by-count":70,"title":["A Comparative Study of Statistical and Rough Computing Models in Predictive Data Analysis"],"prefix":"10.4018","volume":"8","author":[{"given":"Debi","family":"Acharjya","sequence":"first","affiliation":[{"name":"School of Computing Science and Engineering, VIT University, Vellore, India"}]},{"given":"A.","family":"Anitha","sequence":"additional","affiliation":[{"name":"School of Information Technology and Engineering, VIT University, Vellore, India"}]}],"member":"2432","reference":[{"issue":"2","key":"IJACI.2017040103-0","first-page":"95","article-title":"Comparative Study of Rough Sets on Fuzzy Approximation Spaces and Intuitionistic Fuzzy Approximation Spaces.","volume":"4","author":"D. 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