Filtering and Smoothing Via Estimating Functions
研究了状态空间模型中的滤波与平滑问题，不假设分布形式，利用估计函数理论得到最优估计方程，在非高斯情形下优于现有半参数方法。
Abstract We consider the problem of filtering and smoothing in state-space models, which include nonlinear and non-Gaussian models. We do not make any distributional assumptions about the processes involved. Our approach to these problems is based on the theory of estimating functions. Filter and smoother are obtained as solutions of estimating equations that are optimal in appropriate classes. We illustrate our procedures by simulation studies of a model where the observational variance depends on the state and a binomial logit model with a covariate. In non-Gaussian cases, procedures based on estimating equations often perform considerably better than the existing semiparametric procedures.