List Augmentation with Model Based Multiple Imputation: A Case Study Using a Mixed-Outcome Factor Model
Statistica Neerlandica (2003) Vol. 57, nr. 1, pp. 46–57
12 Pages Posted: 14 Feb 2014
Date Written: 2003
Abstract
This study concerns list augmentation in direct marketing. List augmentation is a special case of missing data imputation. We review previous work on the mixed outcome factor model and apply it for the purpose of list augmentation. The model deals with both discrete and continuous variables and allows us to augment the data for all subjects in a company’s transaction database with soft data collected in a survey among a sample of those subjects. We propose a bootstrap-based imputation approach, which is appealing to use in combination with the factor model, since it allows one to include estimation uncertainty in the imputation procedure in a simple, yet adequate manner. We provide an empirical case study of the performance of the approach to a transaction data base of a bank.
Keywords: factor analysis, simulated likelihood, multiple imputation
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
By Wagner A. Kamakura, Michel Wedel, ...
-
Country and Consumer Segmentation: Multi-Level Latent Class Analysis of Financial Product Ownership
By Tammo H.a. Bijmolt, Leo Paas, ...
-
When are Crm Systems Successful? The Perspective of the User and of the Organization
By G.h. Van Bruggen and Berend Wierenga
-
Approaches to Customer Segmentation
By Bruce Cooil, Lerzan Aksoy, ...
-
Customer Betrayal and Retaliation: When Your Best Customers Become Your Worst Enemies
By Yany Gregoire and Robert Fisher
-
Estimating Models with Binary Dependent Variables: Some Theoretical and Empirical Observations
By Guy Gessner, Wagner A. Kamakura, ...
-
By G. S. Popli and D. N. Rao
-
By Alena Audzeyeva, Barbara Summers, ...
-
By John Dawes