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Model Identification Using Search Linear Models and Search Designs

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Entropy, Search, Complexity

Part of the book series: Bolyai Society Mathematical Studies ((BSMS,volume 16))

Abstract

In designing an experiment, we often assume a model and then find a best design satisfying one or more optimal properties under the assumed model. This approach works well when we are absolutely sure about the assumed model that it will fit the experimental data adequately. In reality, we are rarely sure about a particular model in terms of its effectiveness in describing the data adequately. However, we are normally sure about a set of possible models that would describe the data adequately and one of them would possibly describe the data better than the other models in the set. The pioneering work of Srivastava [33] introduced the search linear model with the purpose of searching for and identifying the correct model from a set of possible models that includes the correct model. This paper focuses on model identification through the use of the search linear models, particularly in addressing the fundamental issues and important challenges in statistical design and analysis of experiments while presenting an overview on this area of research. Two important research areas developed over time using the search linear models are in factorial designs and row-column designs.

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Author information

Authors and Affiliations

  1. University of California, Riverside, USA

    S. Ghosh

  2. Kobe University, Japan

    T. Shirakura

  3. Colorado State University, Fort Collins, USA

    J. N. Srivastava

Authors
  1. S. Ghosh
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  2. T. Shirakura
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  3. J. N. Srivastava
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Editor information

Editors and Affiliations

  1. Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, Reáltanoda u. 13-15, 1053, Budapest, Hungary

    Imre Csiszár , Gyula O. H. Katona  & Gábor Tardos ,  & 

  2. Budapest University of Technology and Economics, Pázmány Péter sétány 1/D, 1117, Budapest, Hungary

    Gábor Wiener

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© 2007 János Bolyai Mathematical Society and Springer-Verlag

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Ghosh, S., Shirakura, T., Srivastava, J.N. (2007). Model Identification Using Search Linear Models and Search Designs. In: Csiszár, I., Katona, G.O.H., Tardos, G., Wiener, G. (eds) Entropy, Search, Complexity. Bolyai Society Mathematical Studies, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32777-6_4

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