论文标题
最大秩相关估计器的确切计算
Exact Computation of Maximum Rank Correlation Estimator
论文作者
论文摘要
在本文中，我们提供了一种计算算法，以使用混合整数编程（MIP）方法获得最大秩相关估计器的全局解决方案。我们通过将所有指标函数转换为以估算的二进制参数并表明它等效于原始问题来构建一个新的约束优化问题。我们还考虑了最佳子集排名预测的应用，并表明可以将原始优化问题重新归为MIP。我们为预测性能度量的尾巴概率得出了非反应约束。我们通过经验示例和蒙特卡洛模拟研究了MIP算法的性能。
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.
