[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Ethics Considerations::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
Indexing and Abstracting



 
..
Social Media

..
Licenses
Creative Commons License
This Journal is licensed under a Creative Commons Attribution NonCommercial 4.0
International License
(CC BY-NC 4.0).
 
..
Similarity Check Systems


..
:: Volume 17, Issue 1 (9-2023) ::
JSS 2023, 17(1): 0-0 Back to browse issues page
A Criterion for Assessment of Search Designs in Robust Parameter Experiments
Mohsen Motavaze * , Hooshang Talebi
Abstract:   (1568 Views)
Production of high-quality products necessitates identifying the most influential factors, among many factors, for controlling and reducing quality variation. In such a setting, the factorial designs are utilized to determine the active factors with maximal information and model an appropriate relation between the factors and the variable of interest. In this regard, robust parameter designs dividing the factors to control- and noise factors are efficient methods for offline quality control for stabilizing the quality variation in the presence of the noise factors. Interestingly, this could be achieved through exploiting active control by noise interactions. One needs to experiment with numerous treatments to detect the active interaction effects. Search designs are suggested to save treatments, and a superior one is recommended among the appropriate ones. To determine the superior design, one needs a design criterion; however, the existing criteria could be more beneficial for robust parameter designs. In this paper, we proposed a criterion to rank the search designs and determine the superior one.
Keywords: Robust parameter design, search design, design-criterion, noise factor
Full-Text [PDF 270 kb]   (1313 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2022/10/27 | Accepted: 2023/09/1 | Published: 2023/07/11
References
1. Atkinson‎, ‎A‎. ‎C‎. and Fedorov‎, ‎V‎. ‎‎‎(1975)‎. ‎The Designs of Experiments for Discriminating Between Two Rival Models‎, Biometrika, 62‎, ‎57-70‎.‎ [DOI:10.1093/biomet/62.1.57]
2. Chowdhury, S., Lukemire, J., and Mandal, A. (2020). A-ComVar: A Flexible Extension of Common Variance Designs, Journal of Statistical Theory and Practice, ‎14(1)‎, ‎1-49‎.‎‎‎ [DOI:10.1007/s42519-019-0079-y]
3. Del Castillo, E., Alvarez, M. J., Ilzarbe, L., and Viles, E. (2007). A New Design Criterion for Robust Parameter Experiments. Journal of Quality Technology, 39(3)‎, 279-295‎.‎ [DOI:10.1080/00224065.2007.11917693]
4. ‎ Esmailzadeh, N., and Talebi, H. ‎‎‎(2009). Post-Stage Search Property of the 12-run Plackett-Burman Design. Journal of Statistical Sciences, 2 (2), 149-162.
5. Ghosh‎, ‎S.‎, and ‎Chowdhury‎, ‎S‎. ‎(2017)‎. ‎CV‎, ‎ECV‎, ‎and Robust CV Designs for Replications under a Class of Linear Models in Factorial Experiments‎. Journal of Statistical Planning and Inference‎, 188, ‎1-7‎. [DOI:10.1016/j.jspi.2017.03.004]
6. ‎Ghosh‎, ‎S.‎, and ‎Flores‎, ‎A‎. ‎(2013)‎. Common Variance Fractional Factorial Designs and Their Optimality to Identify a Class of Models‎. Journal of Statistical Planning and Inference‎, 143(10)‎, ‎1807-1815‎.‎‎ [DOI:10.1016/j.jspi.2013.06.008]
7. Kackar, R. N. (1985). Off-line Quality Control, Parameter Design, and the Taguchi Method. Journal of Quality Technology, 17(4)‎, 176-188‎.‎‎ [DOI:10.1080/00224065.1985.11978964]
8. ‎Lopez-Fidalgo‎, ‎J‎. ‎Tommasi‎, ‎C‎. ‎and Trandafir‎, ‎P.C‎. ‎(2007)‎. An Optimal Experimental Design Criterion for Discrimination Between Non-Normal Models‎, J‎. ‎R‎. ‎Statist‎. ‎Soc‎, ‎B69‎, ‎231-242‎.‎ [DOI:10.1111/j.1467-9868.2007.00586.x]
9. Miro, G., and Del Castillo, E. (2004)‎, Two Approaches for Enhancing the Dual Response Approach to Robust Parameter Design‎, Journal of Quality Technology‎, 36(2)‎, 154-168‎.‎‎‎ [DOI:10.1080/00224065.2004.11980262]
10. Montgomery, D. C. (2017). Design and Analysis of Experiments. John Wiley Sons.
11. Mukerjee, R., and Wu, C. F. (2006), A Modern Theory of Factorial Design. New York: Springer.
12. Myers, R. H., D. C. Montgomery, and C. M. Anderson-Cook (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 4th edition. Wiley, New York.
13. Robinson, T. J., Borror, C. M., and Myers, R. H. (2004). Robust Parameter Design: A Review. Quality and reliability engineering international, 20(1), 81-101. [DOI:10.1002/qre.602]
14. Sadeghi, S. and Talebi, H. (2020), Bayesian Criteria for Non-Zero Effects Detection Under Skew-Normal Search Model, REVSTAT-Statistical Journal, 18(3), 311-323.
15. Shirakura, T. Takahashi, T. and Srivastava, J. N. (1996). Searching Probabilities fsor Case, Ann. Statist, 24, no. 6, 2560-2568. [DOI:10.1214/aos/1032181169]
16. Srivastava, J.N. (1975). Designs for Searching Non-Negligible Effects, A Survey of Statistical Design and Linear Models, (J.N. Srivastava, ED.), 507-519, North-Holland, Amsterdam.
17. Stigler, S. M. (1971). Optimal Experimental Design for Polynomial Regression. Journal of the American Statistical Association, 66(334), 311-318. [DOI:10.1080/01621459.1971.10482260]
18. Taguchi, G. (1978). Off-Line and On-Line Quality Control Systems, Proceedings of International Conference on Quality Control, Tokyo,Japan.
19. Taguchi, G. (1987), System of Experimental Design. White Plains, NY: Unipub/Kraus.
20. Taguchi, G., and Y. Wu. (1980), Introduction to Off-Line Quality Control. Central Japan Quality Control Association, Nagoya, Japan.
21. Talebi, H., and Esmailzadeh, N. (2011). Using Kullback-Leibler Distance for Performance Evaluation of Search Designs Comparison, Bulletin of the Iranian Mathematical Society, 37(4), 269-279.
22. Talebi, H., and Esmailzadeh, N. (2011). Weighted Searching Probability for Classes of Equivalent Search Designs Comparison. Communications in Statistics-Theory and Methods, 40(4), 635-647. [DOI:10.1080/03610920903391352]
23. Wu, J. and Hamada, M. (2009). Experiments, Planning, Analysis and Optimization, Wiley Series in Probability and Statistics.
24. Wu, C.F.J. and Y. Zhu (2003). Optimal Selection of Single Arrays for Parameter Design Experiments, Statistica Sinica, 13, 1179-1199.
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Motavaze M, Talebi H. A Criterion for Assessment of Search Designs in Robust Parameter Experiments. JSS 2023; 17 (1)
URL: http://jss.irstat.ir/article-1-822-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 17, Issue 1 (9-2023) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

Persian site map - English site map - Created in 0.09 seconds with 45 queries by YEKTAWEB 4710