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Activity Number: 187 - Contributed Poster Presentations: Section on Nonparametric Statistics
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #302914
Title: Non Linear Functional Data Imputation
Author(s): Aniruddha Rajendra Rao*
Companies: Pennsylvania State University
Keywords: Functional Data Analysis; Random Forest; Non parametric; Imputation; Sparse; Modelling

Our approach helps to fit functional data models with sparsely and irregularly sampled data. Also, it overcomes the limitations of current methods which face major challenges in the fitting of more complex nonlinear models. Currently, many models cannot be consistently estimated unless one assumes that the number of observed points per curve grows sufficiently quickly with the sample size, whereas, we show that our approach which is based on Random Forest can produce consistent estimates without such an assumption using multiple imputation. In this method, we average over many unpruned classification or regression trees. Random forest intrinsically constitutes a multiple imputation scheme. Evaluation is done on multiple simulations and real datasets coming from a diverse selection with artificially introduced missing values ranging from 50% to 90%. Additionally, our method exhibits attractive computational efficiency and can cope with high-dimensional data when compared with PACE and MICE.

Authors who are presenting talks have a * after their name.

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