Hybrid EconometricMachine Learning Models for High- Dimensional Data: Robust Approaches to Anomaly Detection and Inference
- Title
- Hybrid EconometricMachine Learning Models for High- Dimensional Data: Robust Approaches to Anomaly Detection and Inference
- Creator
- Jain, Lakshya; Chopra, Puja
- Description
- This chapter, according to the authors, looks at making modeling robust and interpretable when faced with complex, irregular, and contaminated data environments. As empirical research continues to move towards larger datasets having numerous variables and high dependency among variables, along with instances of irregular data, traditional models of analysis can no longer be very effective. The chapter looks at the possibility of having a comprehensive modeling approach by integrating concepts of robustness like bounded sensitivity and stability analysis, along with flexible modeling at the analytical and computational levels. A major focus is on finding influential data points, rare observations, and structural breaks during the modeling process and not after the process is complete. 2026 by IGI Global Scientific Publishing. All rights reserved.
- Source
- Robust Methods for Anomaly Detection in Econometrics;pp.165-208
- Date
- 01-01-2026
- Publisher
- IGI Global
- Coverage
- Jain L., Christ University, India; Chopra P., Christ University, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833738299-9; 979-833738297-5;
- Format
- online
- Language
- English
- Type
- Book chapter
Collection
Citation
Jain, Lakshya; Chopra, Puja, “Hybrid EconometricMachine Learning Models for High- Dimensional Data: Robust Approaches to Anomaly Detection and Inference,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/24935.