A Nonparametric Method for Benchmarking Survey Data via Signal Extraction
提出一种非参数方法估计信号中平稳部分的协方差矩阵，用于通过信号提取进行基准测试，并与回归法和ARIMA信号提取法比较，证明其可行、稳健且效率接近已知真模型的方法。
Abstract This article introduces a nonparametric method to estimate the covariance matrix for the stationary part of the signal (hidden in data), to enable benchmarking via signal extraction. Some discussions and simulations are carried out to compare the proposed benchmarking method to the regression method development by Cholette and Dagum and the signal extraction method developed by Hillmer and Trabelsi suggesting autoregression integrated moving average (ARIMA) models for the signal. The results show that the nonparametric method is feasible, robust, and almost as efficient as the signal extraction method when the true model for the signal is known.