Comparing the Variability Using Louis' Method and Resampling Methods

Journal of Biometrics & Biostatistics

article

GunaratnamB¹  RaiSN¹ 

[1] Department of Bioinformatics and Biostatistics, University of Louisville;Biostatistics Shared Facility, Department of Bioinformatics and Biostatistics, James Graham Brown Cancer Center, University of Louisville

关键词: EM algorithm;    Maximum likelihood estimation;    Incomplete data;    Complete data;    Bootstrap;    Standard deviation;   

来源: Hilaris Publisher

【 摘 要 】

There is a problem when a relatively simple analysis is changed into a complex one just because some of the information is missing. Louis showed how to estimate the standard deviation of maximum likelihood estimate (MLE) for a parameter θ using the missing information. In the meantime, the resampling method is one of the best methods to calculate the standard deviation of sample estimates. In this article, we define and compare the standard deviation of a parameter θ using complete data, incomplete data, and the EM algorithm. As an illustration, we analyze a data from Rao and compare all methods for estimating variability.

【 授权许可】

Unknown