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Algorithms and error estimations for monotone regression on partially preordered sets

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  • Hansohm, Jürgen

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.

Suggested Citation

  • Hansohm, Jürgen, 2007. "Algorithms and error estimations for monotone regression on partially preordered sets," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 1043-1050, May.
    • Handle: RePEc:eee:jmvana:v:98:y:2007:i:5:p:1043-1050
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Wojciech Gamrot, 2013. "Maximum likelihood estimation for ordered expectations of correlated binary variables," Statistical Papers, Springer, vol. 54(3), pages 727-739, August.

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