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Exact Computation of Maximum Rank Correlation Estimator

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  • Youngki Shin
  • Zvezdomir Todorov

Abstract

In this paper we provide a computation algorithm to get a global solution for the maximum rank correlation estimator using the mixed integer programming (MIP) approach. We construct a new constrained optimization problem by transforming all indicator functions into binary parameters to be estimated and show that it is equivalent to the original problem. We also consider an application of the best subset rank prediction and show that the original optimization problem can be reformulated as MIP. We derive the non-asymptotic bound for the tail probability of the predictive performance measure. We investigate the performance of the MIP algorithm by an empirical example and Monte Carlo simulations

Suggested Citation

  • Youngki Shin & Zvezdomir Todorov, 2021. "Exact Computation of Maximum Rank Correlation Estimator," Department of Economics Working Papers 2021-03, McMaster University.
    • Handle: RePEc:mcm:deptwp:2021-03
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    References listed on IDEAS

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    Cited by:

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    2. Xu, Wenchao & Zhang, Xinyu & Liang, Hua, 2024. "Linearized maximum rank correlation estimation when covariates are functional," Journal of Multivariate Analysis, Elsevier, vol. 202(C).

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    More about this item

    Keywords

    mixed integer programming; finite sample property; maximum rank correlation; U-process;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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