An application of nonparametric regression to missing data in large market surveys. Issue 7 (19th May 2018)

• Record Type: Journal Article
• Authors: Madden, Gary; Apergis, Nicholas; Rappoport, Paul; Banerjee, Aniruddha
• Is Part Of: Journal of applied statistics. Volume 45:Issue 7(2018)
• Page Start: 1292
• Page End: 1302
• Publication Date: 2018-05-19
• Subjects: Treatment of missing data -- survey sampling -- tests for missingness -- consumer preferences -- United States; C18 -- C81 -- D12 -- L96 -- O51; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2017.1369498
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Non-response (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this paper is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed MCAR, MAR and NMAR processes, with data removed.