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计数时间序列模型的蒙特卡洛 EM 估计AI 解读

The observations in parameter-driven models for time series of counts are generated from latent unobservable processes that characterize the correlation structure. These models result in very complex likelihoods, and even the EM algorithm, which is usually well suited for problems of this type, involves high-dimensional integration. In this article we discuss a Monte Carlo EM (MCEM) algorithm that uses a Markov chain sampling technique in the calculation of the expectation…

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一阶移动平均模型最大似然估计量的小样本性质AI 解读

In this paper we discuss finite sample properties of the maximum likelihood estimator θ^ in the first-order moving average model. We give a theoretical explanation for the concentration of θ^ values at the invertibility boundary. We derive the exact distribution of θ^ for sample size n=2 which is found to be of mixed type. For general n we give approximations for pr (⁠θ^=1).