Asymptotic Likelihood-Based Prediction Functions
开发了渐近预测函数，用于近似未来观测值的密度并修正参数不确定性，基于Lauritzen和Hinkley的预测似然定义扩展，通过Kullback-Leibler信息损失度量展示了效率性质，并在线性正态模型、非正态模型和ARCH模型中应用。
This paper develops asymptotic prediction functions that approximate the shape of the density of future observations and correct for parameter uncertainty. The functions are based on extensions to a definition of predictive likelihood originally suggested by Lauritzen and Hinkley. The prediction function is shown to possess efficiency properties based on the Kullback-Leibler measure of information loss. Examples of the application of the prediction function and the derivation of relative efficiency are shown for linearnormal models, nonnormal models, and ARCH models.