A non-linear shrinkage estimator of large-dimensional covariance matrices is derived in a setting of auto-correlated samples, thus generalizing the recent formula by Ledoit-P\'{e}ch\'{e}. The calculation is facilitated by random matrix theory. The result is turned into an efficient algorithm, and an associated Python library, shrinkage, with help of Ledoit-Wolf kernel estimation technique. An example of exponentially-decaying auto-correlations is presented.

翻译：非线性缩缩缩值估计大维共变矩阵在与自动孔径相关的样本设置中产生,从而将最近由 Ledoit-P\'{e}ch\'{e} 提供的公式概括化。 随机矩阵理论为计算提供了便利。 结果变成了有效的算法, 以及一个相关的 Python 库, 在 Ledoit- Wolf 内核估测技术的帮助下, 缩缩缩。 展示了一个指数式衰减的自动通缩的示例 。