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Statistical Quality Technologies pp 347–373Cite as

Robust Design in the Case of Data Contamination and Model Departure

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Abstract

In robust design, it is usually assumed that the experimental data are normally distributed and uncontaminated. However, in many practical applications, these assumptions can be easily violated. It is well known that normal model departure or data contamination can result in biased estimation of the optimal operating conditions of the control factors in the robust design framework. In this chapter, we investigate this possibility by examining these estimation effects on the optimal operating condition estimates in robust design. Proposed estimation methodologies for remedying the difficulties associated with data contamination and model departure are provided. Through the use of simulation, we show that the proposed methods are quite efficient when the standard assumptions hold and outperform the existing methods when the standard assumptions are violated.

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Acknowledgements

The work of Professor Ouyang was supported by the National Natural Science Foundation of China under grants NSFC-71811540414 & 71702072 and the Natural Science Foundation for Jiangsu Institutions under grant BK20170810. The work of Professor Park was supported under the framework of the international cooperation program managed by the National Research Foundation of Korea (2018K2A9A2A06019662). The work of Professor Byun was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (2016R1D1A1B03935397).

Author information

Authors and Affiliations

  1. Department of Management Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China

    Linhan Ouyang

  2. Applied Statistics Laboratory, Department of Industrial Engineering, Pusan National University, Busan, South Korea

    Chanseok Park

  3. Department of Industrial and Systems Engineering, Gyeongsang National University, Jinju, Gyeongnam, South Korea

    Jai-Hyun Byun

  4. Statematics Consulting, New York, NY, USA

    Mark Leeds

Authors
  1. Linhan Ouyang
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  2. Chanseok Park
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  3. Jai-Hyun Byun
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  4. Mark Leeds
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Editor information

Editors and Affiliations

  1. Department of Mathematical Sciences, University of South Dakota, Vermillion, SD, USA

    Yuhlong Lio

  2. Department of Statistical Science, Southern Methodist University, Dallas, TX, USA

    Hon Keung Tony Ng

  3. Department of Statistics, Tamkang University, New Taipei, Taiwan

    Tzong-Ru Tsai

  4. Department of Statistics, University of Pretoria, Pretoria, South Africa

    Ding-Geng Chen

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Ouyang, L., Park, C., Byun, JH., Leeds, M. (2019). Robust Design in the Case of Data Contamination and Model Departure. In: Lio, Y., Ng, H., Tsai, TR., Chen, DG. (eds) Statistical Quality Technologies. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-20709-0_15

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