Overview

This repository complements the paper Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data (Zongyu Dai, Zhiqi Bu, Qi Long):

• minngp.py contains two main imputation functions MI_NNGP1 and MI_NNGP2
  • MI_NNGP1 requires the existence of complete cases
  • MI_NNGP2 does not require the existence of complete cases. But it does require an imtial imputation. If there are any complete cases in the data, MI_NNGP1 is used for the initial imputation. Otherwise, MICE is used for the initial imputation. You can customize the initial imputation method.
  • Both MI_NNGP1 and MI_NNGP2 use a three-layer fully-connected neural network with ReLU activation function. Weight and bias standard deviation can be choosen by cross-validation. You can alse customize the neural network structure.
• MI-NNGP experiments.ipynb contains an example to use it

Note: The MI-NNGP functions are a convenient tool for imputing both low and high-dimensional datasets. It was originally developed for imputing multi-omics data in biological and medical research, where the number of patterns in the data is not overly large. However, if the number of patterns is very large, using MI-NNGP may be time-consuming.

reference

Zongyu Dai, Zhiqi Bu, Qi Long: Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data

@article{dai2022multiple,
  title={Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data},
  author={Dai, Zongyu and Bu, Zhiqi and Long, Qi},
  journal={arXiv preprint arXiv:2211.13297},
  year={2022}
}

Dependencies

• Python 3+
• neural_tangents
• jax