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Title: Truncated estimation for varying-coefficient functional linear models Authors:  Hidetoshi Matsui - Shiga University (Japan) [presenting]
Abstract: The focus is on the problem of estimating a varying-coefficient functional linear model, where the predictor is a function of time and the scalar response depends on not only a functional predictor but also an exogenous variable. The aim is to estimate the model so that the functional predictor does not relate to the response after a certain point in time at any value of the exogenous variable. To do so, we apply the sparse regularization to shrink the corresponding domain of the coefficient function towards exactly zero. Simulation studies are conducted to investigate the effectiveness of the proposed method, and we also apply the method to the analysis of crop yield data.