Journal article
A DYNAMIC SCREENING ALGORITHM FOR HIERARCHICAL BINARY MARKETING DATA
The annals of applied statistics, Vol.17(3), pp.2326-2344
09/01/2023
DOI: 10.1214/22-AOAS1720
Abstract
In many applications of business and marketing analytics, predictive models are fit using hierarchically structured data: common characteristics of products, customers, or web pages are represented as categorical variables, and each category can be split up into multiple subcategories at a lower level of the hierarchy. The model may thus contain hundreds of thousands of binary variables, necessitating the use of variable selection to screen out large numbers of irrelevant or insignificant features. We propose a new dynamic screening method, based on the distance correlation criterion, designed for hierarchical binary data. Our method can screen out large parts of the hierarchy at the higher levels, avoiding the need to explore many lower-level features and greatly reducing the computational cost of screening. The practical potential of the method is demonstrated in a case application on user-brand interaction data from Facebook.
Details
- Title: Subtitle
- A DYNAMIC SCREENING ALGORITHM FOR HIERARCHICAL BINARY MARKETING DATA
- Creators
- Yimei Fan - University of Maryland, College ParkYuan Liao - Rutgers, The State University of New JerseyIlya o. Ryzhov - University of Maryland, College ParkKunpeng Zhang - University of Maryland, College Park
- Resource Type
- Journal article
- Publication Details
- The annals of applied statistics, Vol.17(3), pp.2326-2344
- DOI
- 10.1214/22-AOAS1720
- ISSN
- 1932-6157
- eISSN
- 1941-7330
- Publisher
- INST MATHEMATICAL STATISTICS-IMS
- Number of pages
- 19
- Language
- English
- Date published
- 09/01/2023
- Academic Unit
- Economics
- Record Identifier
- 9984936821302771
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