A bayesian approach to binary response curve estimation |
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Authors: | Makio Ishiguro Yosiyuki Sakamoto |
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Abstract: | 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.
The Institute of statistical mathematics |
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