Statistical Inference with Neural Network Imputation for Item Nonresponse
报告人：Jae-kwang Kim, Iowa State University (ISU)
Abstract: We consider the problem of nonparametric imputation using neural network models. Neural network models can capture complex nonlinear trends and interaction effects, making it a powerful tool for predicting missing values under minimum assumptions on the missingness mechanism. Statistical inference with neural network imputation, including variance estimation, is challenging because the basis for function estimation is estimated rather than known. In this paper, we tackle the problem of statistical inference with neural network imputation by treating the hidden nodes in a neural network as data-driven basis functions. We prove that the uncertainty in estimating the basis functions can be safely ignored and hence the linearization method for neural network imputation can be greatly simplified. A simulation study confirms that the proposed approach results in efficient and well-calibrated confidence intervals even when classic approaches fail due to severe nonlinearity and complicated interactions.
Bio: Jae-kwang Kim is a professor in Statistics department at Iowa State University (ISU) . He is a fellow of ASA and IMS and recently named to a Liberal Arts and Sciences Dean's Professor from the college of Liberal Arts and Sciences at Iowa State University.
His main research interest lies in survey sampling and statistical analysis with missing data, and related topics in measurement error, multi-level models, and data integration. His recent work focuses on machine learning topics such as function estimation under reproducing kernel Hilbert space.
Zoom Meeting: https://us02web.zoom.us/j/87187304700
Meeting ID：871 8730 4700