A bayesian approach to binary response curve estimation
 Makio Ishiguro,
 Yosiyuki Sakamoto
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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 (hyperparameters) 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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 Title
 A bayesian approach to binary response curve estimation
 Journal

Annals of the Institute of Statistical Mathematics
Volume 35, Issue 1 , pp 115137
 Cover Date
 19831201
 DOI
 10.1007/BF02480969
 Print ISSN
 00203157
 Online ISSN
 15729052
 Publisher
 Kluwer Academic Publishers
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