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    A convergent algorithm for a generalized multivariate isotonic regression problem

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    Hu_2012 (153.0Kb)
    Date
    2012-02
    Author
    Hansohm, Juergen
    Hu, Xiaomi
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    Citation
    Hansohm, Jèurgen, and Xiaomi Hu. 2012. "A convergent algorithm for a generalized multivariate isotonic regression problem". Statistical Papers. 53 (1): 107-115.
    Abstract
    Sasabuchi et al. (Biometrika 70(2):465-472, 1983) introduces a multivariate version of the well-known univariate isotonic regression which plays a key role in the field of statistical inference under order restrictions. His proposed algorithm for computing the multivariate isotonic regression, however, is guaranteed to converge only under special conditions (Sasabuchi et al., J Stat Comput Simul 73(9):619-641, 2003). In this paper, a more general framework for multivariate isotonic regression is given and an algorithm based on Dykstra's method is used to compute the multivariate isotonic regression. Two numerical examples are given to illustrate the algorithm and to compare the result with the one published by Fernando and Kulatunga (Comput Stat Data Anal 52:702-712, 2007).
    Description
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    URI
    http://hdl.handle.net/10057/5132
    http://dx.doi.org/10.1007/s00362-010-0317-6
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