Two sided analysis of variance with a latent time series
Many real life regression problems exhibit some kind of calender time dependency and it is often of interest to predict the behavior of the regression function along this calender time direction. This can be formulated as a regression model with an added latent time series and the task is to be able to analyse this series. In this paper we engage this through a two step procedure, firstly we treat the time dependent elements as parameters and estimate them in the two-sided analysis of variance setup, secondly we use the estimated time series as predictor of the latent time series. An application to risk theory is discussed.
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