Much Ado About Nothing
Top Cited Papers
- 1 February 2007
- journal article
- Published by Taylor & Francis in The American Statistician
- Vol. 61 (1) , 79-90
- https://doi.org/10.1198/000313007x172556
Abstract
Missing data are a recurring problem that can cause bias or lead to inefficient analyses. Statistical methods to address missingness have been actively pursued in recent years, including imputation...Keywords
This publication has 46 references indexed in Scilit:
- Analyzing Incomplete Discrete Longitudinal Clinical Trial DataStatistical Science, 2006
- Missing-Data Methods for Generalized Linear ModelsJournal of the American Statistical Association, 2005
- Last observation carry‐forward and last observation analysisStatistics in Medicine, 2004
- A Potential for Bias When Rounding in Multiple ImputationThe American Statistician, 2003
- Multiple Imputation for Missing DataSociological Methods & Research, 2000
- Multiple Imputation After 18+ YearsJournal of the American Statistical Association, 1996
- Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear RegressionJournal of the American Statistical Association, 1996
- Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing DataJournal of the American Statistical Association, 1995
- Regression With Missing X's: A ReviewJournal of the American Statistical Association, 1992
- Multivariate Correlation Models with Mixed Discrete and Continuous VariablesThe Annals of Mathematical Statistics, 1961