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Approximate cost-efficient sequential designs for binary response models with application to switching measurements

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  • Karvanen, Juha

Abstract

The efficiency of an experimental design is ultimately measured in terms of time and resources needed for the experiment. Optimal sequential (multi-stage) design is studied in the situation where each stage involves a fixed cost. The problem is motivated by switching measurements on superconducting Josephson junctions. In this quantum mechanical experiment, the sequences of current pulses are applied to the Josephson junction sample and a binary response indicating the presence or the absence of a voltage response is measured. The binary response can be modeled by a generalized linear model with the complementary log-log link function. The other models considered are the logit model and the probit model. For these three models, the approximately optimal sample size for the next stage as a function of the current Fisher information and the stage cost is determined. The cost-efficiency of the proposed design is demonstrated in simulations based on real data from switching measurements. The results can be directly applied to switching measurements and they may lead to substantial savings in the time needed for the experiment.

Suggested Citation

  • Karvanen, Juha, 2009. "Approximate cost-efficient sequential designs for binary response models with application to switching measurements," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1167-1176, February.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1167-1176
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    References listed on IDEAS

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    1. Juha Karvanen & Juha J. Vartiainen & Andrey Timofeev & Jukka Pekola, 2007. "Experimental designs for binary data in switching measurements on superconducting Josephson junctions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(2), pages 167-181, March.
    2. Dorta-Guerra, Roberto & González-Dávila, Enrique & Ginebra, Josep, 2008. "Two-level experiments for binary response data," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 196-208, September.
    3. Biedermann, Stefanie & Dette, Holger & Zhu, Wei, 2006. "Optimal Designs for DoseResponse Models With Restricted Design Spaces," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 747-759, June.
    4. Tekle, Fetene B. & Tan, Frans E.S. & Berger, Martijn P.F., 2008. "Maximin D-optimal designs for binary longitudinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5253-5262, August.
    5. Dror, Hovav A. & Steinberg, David M., 2008. "Sequential Experimental Designs for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 288-298, March.
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