A Non-Gaussian State Space Model and Application to Prediction of Records
开发了一类适用于删失数据的状态空间模型，假设观测值在给定未观测状态变量条件下服从指数分布，并通过变换进行推广，应用于记录预测，以田径数据为例说明。
SUMMARY We develop a class of state space models for censored data. The basic model assumes an exponential distribution for the observations, conditionally on unobserved state variables. The model may be generalised by allowing transformations. We develop an application to the prediction of records. This is illustrated with some athletics data, though we also discuss briefly the possibility of more general applications connected with extreme values.