International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064


Downloads: 106

Research Paper | Statistics | Nigeria | Volume 5 Issue 9, September 2016


Application of Constrained Optimization Approach to Missing Data in Experimental Design

Michael Ekholuenetale, Adamson O. Ajakaiye


Abstract: During the course of a research data obtained may be fully observed or particularly observed. If the data obtained from a research is partially observed, then a common problem in experiment has occurred. This problem is known as the missing observation. Missing observation infers that no data (value) is stored for the variable in the current observation. Missing data are recurring in all sorts of research irrespective of the field, science, medical, agricultural and social science and so on. Researchers are faced with the problem of partially observed data sets. There are several reasons why data may be missing. They may be missing due to failure to record, gross errors in recording, accident and death amongst others. Missing data are very sensitive issues and many analyses techniques cannot proceed with gaps in their data. These missing values must be estimated and replaced before the analysis can be completed.


Keywords: Constrained Optimization, Missing observation, ANOVA, Design of Experiment


Edition: Volume 5 Issue 9, September 2016,


Pages: 1144 - 1149


How to Cite this Article?

Michael Ekholuenetale, Adamson O. Ajakaiye, "Application of Constrained Optimization Approach to Missing Data in Experimental Design", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=ART20161644, Volume 5 Issue 9, September 2016, 1144 - 1149

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