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Algorithms and error estimations for monotone regression on partially preordered sets

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  • Hansohm, Jürgen
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    Abstract

    Monotone (or isotonic) regression plays an important role in data analysis and in other fields. In many cases the monotonicity is only defined for a partial instead of a total preorder. No efficient algorithm is known which solves the general problem in a finite number of steps. For an approximate solution of the optimum some error estimations are given. Moreover, some new results concerning monotone regression and the treatment of missing values are presented in this paper.

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    File URL: http://www.sciencedirect.com/science/article/B6WK9-4MNJ2RW-1/2/094624231f9741b79c44510cad160c96
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 98 (2007)
    Issue (Month): 5 (May)
    Pages: 1043-1050

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    Handle: RePEc:eee:jmvana:v:98:y:2007:i:5:p:1043-1050

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    Related research

    Keywords: Monotone regression Isotone regression Projection Partial order Least squares solution;

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    Cited by:
    1. Wojciech Gamrot, 2013. "Maximum likelihood estimation for ordered expectations of correlated binary variables," Statistical Papers, Springer, vol. 54(3), pages 727-739, August.
    2. Jürgen Hansohm & Xiaomi Hu, 2012. "A convergent algorithm for a generalized multivariate isotonic regression problem," Statistical Papers, Springer, vol. 53(1), pages 107-115, February.

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