Maximum Likelihood Estimation of Order m for Stationary Stochastic Processes
针对似然函数难以计算的问题，提出用最近m个观测值的条件密度近似对数似然，给出估计量的渐近性质，并通过数值比较验证效果。
SUMMARY Sometimes the likelihood cannot be computed even introducing approximations whose effect is asymptotically negligible. To overcome this problem for inference from a stochastic process, we approximate the log likelihood by a sum whose generic term is the density function of the corresponding sample element conditional on the m most recent observations, for some m ≥ 0. General results on the asymptotic properties of the associated estimates are given, and numerical comparisons with other estimators are carried out in special cases.