Annals of the Institute of Statistical Mathematics

, Volume 35, Issue 1, pp 115–137

A bayesian approach to binary response curve estimation

  • Makio Ishiguro
  • Yosiyuki Sakamoto
Article

DOI: 10.1007/BF02480969

Cite this article as:
Ishiguro, M. & Sakamoto, Y. Ann Inst Stat Math (1983) 35: 115. doi:10.1007/BF02480969

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.

Copyright information

© Kluwer Academic Publishers 1983

Authors and Affiliations

  • Makio Ishiguro
  • Yosiyuki Sakamoto

There are no affiliations available