A convergent algorithm for a generalized multivariate isotonic regression problem |
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Authors: | Jürgen Hansohm Xiaomi Hu |
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Institution: | (1) Univ. Naples ‘Federico II’ and CPS, Naples, Italy |
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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). |
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Keywords: | |
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