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Title: Scalar on image regression with nonignorable missing data Authors:  Xinyuan Song - Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: A scalar-on-image regression model is considered that uses ultrahigh dimensional imaging data as explanatory covariates. The model is used to investigate important risk factors for the scalar response of interest, which is subject to non-ignorable missingness. We propose the use of an efficient functional principal component analysis method to reduce the dimensions of the imaging observations. Given that non-ignorable non-response distorts the accuracy of statistical inference and generates misleading results, we propose an imaging exponential tilting model for the examination of the potential influence of imaging observations along with scalar variables on the probability of missingness. An instrumental variable, such as a covariate associated with the response but conditionally independent of the probability of missingness, is introduced to facilitate model identifiability. Statistical inference is conducted in a Bayesian framework with Markov chain Monte Carlo algorithms. Simulation studies show that the proposed method exhibits satisfactory finite sample performance. The methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative dataset.