On the Estimation of Residual Variance and Order in Autoregressive Time Series
研究了自回归过程中残差方差的Yule-Walker、最小二乘和Burg型估计的偏差，发现Yule-Walker估计较差，并探讨了对阶数确定的影响，将AIC准则的过度估计结果推广到多变量情形。
SUMMARY We study the bias of Yule-Walker, least squares and Burg-type estimates of the residual variance of autoregressive processes. Both simulations and theory indicate that Yule-Walker estimates are inferior to least squares and Burg-type estimates. The effect on order determination is also studied, and we extend the results on overestimation of the AIC criterion to the general multivariate case. For strongly autocorrelated processes, Yule-Walker estimates of residual variance and order may be severely biased even for comparatively large sample sizes.