Integrative Interaction Analysis using Threshold Gradient Directed Regularization
Yang Li1,2, Rong Li2, Yichen Qin3
1Center for Applied Statistics, Renmin University of China.
Applied Stochastic Models in Business and Industry
|October 19, 2020
Summary
Integrative analysis improves interaction analysis for complex business problems by jointly analyzing multiple datasets. A modified Threshold Gradient Directed Regularization (TGDR) approach effectively selects important interactions and main effects.
Area of Science:
- Business Analytics
- Statistical Modeling
- Data Science
Background:
- High-dimensional data is common in business, with interactions often more significant than main effects.
- Analyzing single datasets for interactions is often insufficient due to a large number of parameters.
- Integrative analysis of multiple datasets outperforms single-dataset and meta-analysis methods.
Purpose of the Study:
- To conduct integrative analysis specifically for interaction analysis.
- To develop a robust method for regularized estimation and selection of interactions and main effects.
Main Methods:
- Applied a modified Threshold Gradient Directed Regularization (TGDR) approach.
- Ensured the TGDR approach respects the hierarchy of main effects and interactions.
- Utilized simulations and real-world data for validation.
Main Results:
- The modified TGDR approach demonstrated satisfactory practical performance.
- The method is computationally simple, broadly applicable, and intuitively formulated.
- Successful application to financial early warning systems and news-APP recommendation data.
Conclusions:
- Integrative interaction analysis using the modified TGDR approach is effective.
- The method offers a powerful tool for uncovering complex relationships in high-dimensional business data.
- The approach provides reliable insights beyond single-dataset limitations.
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