Penalized optimal scoring for the classification of multi-dimensional functional data

To overcome this limitation, we use penalized optimal scoring to construct a new method for the classification of multi-dimensional functional data. The proposed method consists of two stages. First, the series of observed discrete values available for each individual are expressed as a set of continuous curves. Next, the penalized optimal scoring model is estimated on the basis of these curves. A similar penalized optimal scoring method was described in my previous work, but this model is not suitable for the analysis of continuous functions. In this paper we adopt a Gaussian kernel approach to extend the previous model. The high accuracy of the new method is demonstrated on Monte Carlo simulations, and used to predict defaulting firms on the Japanese Stock Exchange.