253 – Contributed Poster Presentations: Section on Statistical Computing
Caveats on Data Cloning
Brian Zaharatos
University of Colorado Boulder
William Navidi
Colorado School of Mines
For the maximum likelihood estimator (MLE) to be unique, the parameter must be both identifiable and estimable. A parameter is identifiable if there is a one-to-one correspondence between parameter values and density functions. A parameter is estimable if the likelihood function has a unique mode. The method of data cloning has been proposed as a way to diagnose structural deficiencies—such as non-identifiability and inestimability—in a model. In this paper, we discuss cases in which the number of clones required to detect model deficiencies may be impractically large, and provide guidelines for avoiding such cases.