A non-parametric predictive model for missing data: A case of Philippine public hospitals

College

Gokongwei College of Engineering

Department/Unit

Industrial Engineering

Document Type

Archival Material/Manuscript

Abstract

Organizations have an abundance of data but actually have incomplete information. Incomplete information happens when there is missing or unreliable data which can lead to wrong decisions. A predictive model was developed using Hurwicz criterion by means of linear programming (LP). This predictive model estimates the organization's missing or unreliable data using external data from other similar organizations. The proposed predictive model was tested using data from Philippine public hospitals under the Department of Health. The model was able to provide a range of data can test the validity of incomplete information encompassing the optimistic and pessimistic decisions made by the organization.

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Disciplines

Operations Research, Systems Engineering and Industrial Engineering

Note

Paper presented at the 2011 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia, January 22-24, 2011

Keywords

Information resources management

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