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A Projection Approach to Monotonic Regression with Bernstein Polynomials

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Abstract

Monotonic regression problems have been widely seen in many fields like economics and biostatistics. Usually the monotonic parameter space is used by the Bayesian methods using Bernstein polynomials. In this paper the authors extend the usual parameter space to a larger space in which all the proper parameters making the regression function to be monotonic are included. In order to ensure that the problem could be solved in the new parameter space, the authors use a projection posterior method to make inference. The authors show the proposed method has good approximation properties and performs well compared with other competing methods both in simulations and in practical applications.

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Authors and Affiliations

  1. School of Mathematical Sciences, Peking University, Beijing, 100871, China

    Guo Zhu & Xiangzhong Fang

Authors
  1. Guo Zhu
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  2. Xiangzhong Fang
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Corresponding authors

Correspondence to Guo Zhu or Xiangzhong Fang.

Additional information

This research was supported by Science Challenge Project under Grant No. TZ2018001.

This paper was recommended for publication by Editor SUN Liuquan.

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Zhu, G., Fang, X. A Projection Approach to Monotonic Regression with Bernstein Polynomials. J Syst Sci Complex 35, 1910–1928 (2022). https://doi.org/10.1007/s11424-022-0321-7

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  • DOI: https://doi.org/10.1007/s11424-022-0321-7

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