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谱密度的数据驱动有效估计AI 解读

A nonparametric data-driven spectral density estimator is suggested for a class of processes with the exponentially decaying autocovariance function. This particular class is motivated by causal ARMA processes. The estimator is asymptotically efficient; that is, its mean integrated squared error converges with optimal minimax constant and rate as the sample size increases. The article also presents a Monte Carlo study of the estimator for the case of small sample sizes and…

数据驱动的有效谱密度估计AI 解读

A nonparametric data-driven spectral density estimator is suggested for a class of processes with the exponentially decaying autocovariance function. This particular class is motivated by causal ARMA processes. The estimator is asymptotically efficient; that is, its mean integrated squared error converges with optimal minimax constant and rate as the sample size increases. The article also presents a Monte Carlo study of the estimator for the case of small sample sizes and…