Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models
提出一类单随机源的非线性状态空间模型，以乘法Holt-Winters方法为特例，研究基于指数平滑而非卡尔曼滤波的最大似然估计，并给出预测区间计算方法，在模拟和真实数据上验证。
Abstract A class of nonlinear state-space models, characterized by a single source of randomness, is introduced. A special case, the model underpinning the multiplicative Holt-Winters method of forecasting, is identified. Maximum likelihood estimation based on exponential smoothing instead of a Kalman filter, and with the potential to be applied in contexts involving non-Gaussian disturbances, is considered. A method for computing prediction intervals is proposed and evaluated on both simulated and real data.