Local asymptotic normality property for fractional Gaussian noise under high-frequency observations
证明了高频观测下分数高斯噪声的局部渐近正态性，其中速率矩阵非对角且依赖于待估参数；基于此性质，最大似然估计序列渐近有效，Hurst参数的似然比检验是渐近一致最优势无偏检验。
Local Asymptotic Normality (LAN) property for fractional Gaussian noise under high-frequency observations is proved with nondiagonal rate matrices depending on the parameter to be estimated. In contrast to the LAN families in the literature, nondiagonal rate matrices are inevitable. As consequences of the LAN property, a maximum likelihood sequence of estimators is shown to be asymptotically efficient and the likelihood ratio test on the Hurst parameter is shown to be an asymptotically uniformly most powerful unbiased test for two-sided hypotheses.