Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications

TL;DR

This paper introduces a deep learning framework using CNNs to automate key statistical analysis tasks like model selection and parameter estimation, significantly aiding big data applications.

Contribution

It presents novel CNN-based neural models for automating statistical analysis tasks, improving efficiency and accuracy over traditional manual methods.

Findings

CNNs achieve high accuracy in model selection

Neural estimators provide precise parameter estimates

Framework can be extended to automate entire SA process

Abstract

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.