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This work presents a new approach, called Multiple Imputation of Sparsely‐sampled Functions at Irregular Times (MISFIT), for fitting generalized functional linear regression models with sparsely and irregularly sampled data. Current methods do not allow for consistent estimation unless one assumes that the number of observed points per curve grows sufficiently quickly with the sample size. In contrast,...
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