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Multiple Imputation for the Fatal Accident Reporting System
Author(s) -
Heitjan Daniel F.,
Little Roderick J. A.
Publication year - 1991
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2347902
Subject(s) - imputation (statistics) , accident (philosophy) , statistics , computer science , data mining , missing data , mathematics , philosophy , epistemology
SUMMARY The Fatal Accident Reporting System (FARS) is a database collected for the US National Highway Traffic Safety Administration (NHTSA) at the site of all fatal traffic accidents. Variables include location and time of accident, number and position of vehicles, age, sex and driving record of the driver, seat‐belt use and blood alcohol content of the driver. The last two variables are of great interest but have substantial proportions of missing data. The NHTSA is interested in a method of imputation that allows appropriate estimates and standard errors to be computed from the filled‐in data. This paper explores the use of multiple imputation based on predictive mean matching as a means of achieving these goals. Two specific methods are described and applied to a sample of the FARS data. A simulation study compares the frequency properties of the methods.