Main Article Content
Abstract
We illustrate with examples when and how maximum likelihood estimators continue to be asymptotically efficient even under misspecified models. Also, we provide a necessary and sufficient condition under which a subset of the vector of MLE's retains its asymptotic efficiency under misspecified models even though the MLE itself is not fully asymptotic efficient.
Keywords
Asymptotic
Maximum Likelihood
Misspecified Models
Article Details
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
Ghosh, M., & Song, J. (2012). Asymptotic Efficiency of Maximum Likelihood Estimators Under Misspecified Models. Pakistan Journal of Statistics and Operation Research, 8(3), 537-542. https://doi.org/10.18187/pjsor.v8i3.525