标题： 分析估计过程的统计与计算权衡 显示英文标题

标题： Analyzing statistical and computational tradeoffs of estimation procedures

摘要： 最近数据量和维度的爆炸性增长加剧了仔细权衡计算效率和统计效率的必要性，从而使推断既可行又有意义。 我们提出一个框架，为实践者提供了一个明确的机会，说明他们在给定计算成本下愿意接受多少统计风险，并为任何给定的推断问题带来理论上的风险-计算前沿。 我们展示了风险与计算之间的权衡，并在三个不同的设置中展示了这一前沿。 首先，我们推导了在经典设定中估计正态分布数据的均值和方差的参数风险的解析形式，以及在指数族参数的更一般设定中的风险解析形式。 第二个例子集中在计算受限的Hodges-Lehmann估计量上。 最后，我们在线性回归的背景下评估了迭代矩阵求逆算法提前终止相关的风险。 显示英文摘要

摘要： The recent explosion in the amount and dimensionality of data has exacerbated the need of trading off computational and statistical efficiency carefully, so that inference is both tractable and meaningful. We propose a framework that provides an explicit opportunity for practitioners to specify how much statistical risk they are willing to accept for a given computational cost, and leads to a theoretical risk-computation frontier for any given inference problem. We illustrate the tradeoff between risk and computation and illustrate the frontier in three distinct settings. First, we derive analytic forms for the risk of estimating parameters in the classical setting of estimating the mean and variance for normally distributed data and for the more general setting of parameters of an exponential family. The second example concentrates on computationally constrained Hodges-Lehmann estimators. We conclude with an evaluation of risk associated with early termination of iterative matrix inversion algorithms in the context of linear regression.