Abstract

Modern scientific instruments readily record various dynamical phenomena at high frequency and for extended durations. Spanning timescales across several orders of magnitude, such “high-throughput” (HTP) data are routinely analyzed with parametric models in the frequency domain. However, the large size of HTP datasets can render maximum likelihood estimation prohibitively expensive. Moreover, HTP recording devices are operated by extensive electronic circuitry, producing periodic noise to which parameter estimates are highly sensitive. This article proposes to address these issues with a two-stage approach. Preliminary parameter estimates are first obtained by a periodogram variance-stabilizing procedure, for which data compression greatly reduces computational costs with minimal impact to statistical efficiency. Next, a novel test with false discovery rate control eliminates most periodic outliers, to which the second-stage estimator becomes more robust. Extensive simulations and experimental results indicate that for a widely-used model in HTP data analysis, a substantial reduction in mean squared error can be expected by applying our methodology.

摘要

现代科学仪器可以随时随地记录高频率的各种动力学现象。跨越几个数量级的跨度时标，通常使用频域中的参数模型来分析此类“高吞吐量”（HTP）数据。但是，大尺寸的HTP数据集可能会使最大似然估计的成本过高。此外，HTP记录设备由广泛的电子电路操作，产生周期性的噪声，其参数估计高度敏感。本文建议采用两个阶段的方法来解决这些问题。首先通过周期图方差稳定过程获得初步的参数估计值，该过程的数据压缩可大大降低计算成本，并且对统计效率的影响最小。下一个，具有错误发现速率控制的新颖测试消除了大多数周期性离群值，第二阶段估计量对此变得更加健壮。大量的仿真和实验结果表明，对于在HTP数据分析中广泛使用的模型，可以通过应用我们的方法来预期均方误差的显着降低。