Name
Title | ||
|---|---|---|
Confidence intervals for a common mean with missing data with applications in an AIDS study |
Abstract | ||
|---|---|---|
In practical data analysis, nonresponse phenomenon frequently occurs. In this paper, we propose an empirical likelihood based confidence interval for a common mean by combining the imputed data, assuming that data are missing completely at random. Simulation studies show that such confidence intervals perform well, even when the missing proportion is high. Our method is applied to an analysis of a real data set from an AIDS clinic trial study. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.csda.2008.09.021 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
simulation study,nonresponse phenomenon,aids clinic trial study,empirical likelihood,confidence interval,practical data analysis,missing data,imputed data,common mean,aids study,missing proportion,data,clinical trial,data analysis | Econometrics,Mean estimation,AIDS Study,Robust confidence intervals,Empirical likelihood,Missing data,Imputation (statistics),Statistics,Confidence interval,Trial study,Mathematics | Journal |
Volume | Issue | ISSN |
53 | 2 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
1 | 0.43 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Liang | 1 | 22 | 4.48 |
| Haiyan Su | 2 | 2 | 1.21 |
| Guohua Zou | 3 | 12 | 5.72 |