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Sequential asymmetric third order rotatable designs (SATORDs).

M Hemavathi1,2, Eldho Varghese2,3, Shashi Shekhar1,3

  • 1Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a cost-effective method for creating asymmetric third-order rotatable designs (TORDs). By augmenting existing asymmetric second-order rotatable designs (ASORDs), researchers can efficiently upgrade to a third-order model without losing initial experimental data.

Keywords:
Response surface methodologyasymmetricorthogonal transformationrotatabilitysequential experimentationthird order designs

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Area of Science:

  • Statistics
  • Experimental Design
  • Industrial Optimization

Background:

  • Traditional rotatable designs are often symmetric, limiting their applicability in practical scenarios with mixed factor levels.
  • Asymmetric second-order rotatable designs (ASORDs) offer broader exploration but may suffer from significant lack of fit, necessitating higher-order models.
  • Developing new third-order rotatable designs (TORDs) can be expensive, potentially underutilizing initial experimental data.

Purpose of the Study:

  • To propose an economical method for constructing asymmetric third-order rotatable designs (TORDs).
  • To enable sequential augmentation of existing asymmetric second-order rotatable designs (ASORDs) to achieve TORDs.
  • To avoid discarding initial experimental runs when upgrading to a higher-order model.

Main Methods:

  • Sequential augmentation of asymmetric second-order rotatable designs (ASORDs) with additional points.
  • Construction of asymmetric third-order rotatable designs (TORDs) by building upon existing ASORDs.
  • Utilizing initial stage runs for second-order model fitting and subsequent runs for third-order model fitting.

Main Results:

  • The proposed method allows for the construction of asymmetric TORDs.
  • The augmentation strategy is economical, leveraging previously collected data.
  • Initial experimental runs are not discarded, improving efficiency.

Conclusions:

  • The developed method provides a cost-effective approach to obtaining asymmetric TORDs.
  • This approach enhances the utility of experimental data by allowing for model order upgrades.
  • Researchers can achieve optimal response estimation more efficiently using the proposed sequential augmentation technique.