Summary
The purpose of the present paper is to propose a practical procedure for the estimation of the binary response curve. The procedure is based on a model which approximates the response curve by a finely segmented piecewise constant function. To obtain a stable estimate we assume a prior distribution of the parameters of the model. The prior distribution has several parameters (hyper-parameters) which are chosen to minimize an information criterion ABIC. The procedure is applicable to data consisting of observations of a binary response variable and a single explanatory variable. The practical utility of the procedure is demonstrated by examples of applications to the dose response curve estimation, to the intensity function estimation of a point process and to the analysis of social survey data. The application of the procedure to the discriminant analysis is also briefly discussed.
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Ishiguro, M., Sakamoto, Y. A bayesian approach to binary response curve estimation. Ann Inst Stat Math 35, 115–137 (1983). https://doi.org/10.1007/BF02480969
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DOI: https://doi.org/10.1007/BF02480969