The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficient Sequential Designs with Binary Data
Abstract
A class of sequential designs for estimating the percentiles of a quantal response curve is proposed. Its updating rule is based on an efficient summary of all of the data available via a parametric model. The logit-MLE version of the proposed designs can be viewed as a natural analog of the Robbins—Monro procedure in the case of binary data. It is shown to be asymptotically consistent, optimal, and nonparametric via its connection with the latter procedure. For certain choices of initial designs, the proposed method performs very well in a simulation study for sample sizes up to 35. A nonparametric sequential design, via the Spearman—Kärber estimator, for estimating the median is also proposed.
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Citations
Cites
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Cited by
Khuri, A. I., & Mukhopadhyay, S. (2010) · WIREs Computational Statistics · doi:10.1002/wics.73 · Cited by 2,113
Optimal Design for Multinomial Choice Experiments
Kanninen, B. J. (2002) · Journal of Marketing Research · doi:10.1509/jmkr.39.2.214.19080 · Cited by 305
Metadata
- Authors
- Wu, C. F. J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1985-12
- Volume
- 80
- Issue
- 392
- Pages
- 974-984
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2102241292
- Cited by
- 206
- References
- 1
- FWCI
- 1.93
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wu, C. F. J. (1985). Efficient Sequential Designs with Binary Data. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1985.10478213
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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