Journal article
A Diagnostic for Assessing the Influence of Cases on the Prediction of Missing Data
Journal of the Royal Statistical Society: Series D (The Statistician), Vol.50(4), pp.427-440
12/2001
DOI: 10.1111/1467-9884.00288
Abstract
An important aspect of statistical modelling involves the identification of cases that have a significant influence on certain inferential results. In modelling problems where data are missing, the predicted values for the missing observations are frequently of interest. To assist in the identification of cases that substantially affect these predicted values, we introduce a case deletion diagnostic which is often conveniently evaluated in the setting of the EM algorithm. Our diagnostic is defined as the Kullback–Leibler information between two versions of the conditional density of the missing data given the observed data: one based on the parameter estimates arising from the full data set; the other based on the parameter estimates arising from the case-deleted data set. We outline the computation of the diagnostic for two Gaussian frameworks: for bivariate data applications in which some of the data pairs are incomplete, and for time series forecasting applications in which the missing observations are future realizations of the series. Our analyses involve bivariate data from the 1998 American Major League Baseball season and a time series consisting of cardiovascular mortality readings from the Los Angeles area.
Details
- Title: Subtitle
- A Diagnostic for Assessing the Influence of Cases on the Prediction of Missing Data
- Creators
- Joseph E Cavanaugh - University of Missouri-Columbia, USAJacob J Oleson - University of Missouri-Columbia, USA
- Resource Type
- Journal article
- Publication Details
- Journal of the Royal Statistical Society: Series D (The Statistician), Vol.50(4), pp.427-440
- DOI
- 10.1111/1467-9884.00288
- ISSN
- 0039-0526
- eISSN
- 1467-9884
- Publisher
- Blackwell Publishers Ltd
- Number of pages
- 14
- Language
- English
- Date published
- 12/2001
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984214661102771
Metrics
15 Record Views