Minimum Bias Design for Polynomial Regression
- Authors
- Jang, Dae-Heung; Kim, Youngil
- Issue Date
- Dec-2015
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- bias; minimum bias design; Q-optimal design; integrated mean squared error(IMSE)
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.28, no.6, pp 1227 - 1234
- Pages
- 8
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 28
- Number
- 6
- Start Page
- 1227
- End Page
- 1234
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/64420
- DOI
- 10.5351/KJAS.2015.28.6.1227
- ISSN
- 1225-066X
2383-5818
- Abstract
- Traditional criteria for optimum experimental designs depend on the specifications of the model; however, there will be a dilemma when we do not have perfect knowledge about the model. Box and Draper (1959) suggested one direction to minimize bias that may occur in this situation. We will demonstrate some examples with exact solutions that provide a no-bias design for polynomial regression. The most interesting finding is that a design that requires less bias should allocate design points away from the border of the design space.
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Collections - College of Business & Economics > School of Business Administration > 1. Journal Articles
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