Fitting nonlinear time-series models with applications to stochastic variance models
提出利用EM算法和迭代模拟技术实现非线性时间序列模型的最大似然估计，并将该方法应用于汇率数据的随机方差模型拟合。
New strategies for the implementation of maximum likelihood estimation of nonlinear time series models are suggested. They make use of recent work on the EM algorithm and iterative simulation techniques. The estimation procedures are applied to the problem of fitting stochastic variance models to exchange rate data.