  Statistical analysis (SA) is a complex process to deduce population
properties from analysis of data. It usually takes a well-trained analyst to
successfully perform SA, and it becomes extremely challenging to apply SA to
big data applications. We propose to use deep neural networks to automate the
SA process. In particular, we propose to construct convolutional neural
networks (CNNs) to perform automatic model selection and parameter estimation,
two most important SA tasks. We refer to the resulting CNNs as the neural model
selector and the neural model estimator, respectively, which can be properly
trained using labeled data systematically generated from candidate models.
Simulation study shows that both the selector and estimator demonstrate
excellent performances. The idea and proposed framework can be further extended
to automate the entire SA process and have the potential to revolutionize how
SA is performed in big data analytics.
