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平稳随机过程的 m 阶最大似然估计AI 解读

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…

平稳随机过程的 m 阶最大似然估计AI 解读

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…