Data-Driven Choice of a Spectrum Estimate: Extending the Applicability of Cross-Validation Methods
提出一种从多种谱估计方法中客观选择最优估计的方法，通过引入广义留一谱估计扩展交叉验证技术的适用范围，并针对自回归和Blackman-Tukey估计给出新的平滑参数选择方法，蒙特卡洛研究验证了其有效性。
Abstract I develop methods of objectively choosing a spectrum estimate from a general class C of available estimates. C can, for example, simultaneously include Blackman—Tukey and autoregressive estimates, so the statistician no longer needs to choose one type or the other arbitrarily. The methods work by extending the applicability of existing cross-validatory techniques through the introduction of generalized leave-out-one spectrum estimates. As special cases, I obtain new objective smoothness parameter selection methods for both autoregressive and Blackman—Tukey estimates. In a Monte Carlo study, I demonstrate the effectiveness of the particular methods that result from generalizing Wahba's CVMSE.