A note on maximum likelihood estimation for mixture models

Research output: Contribution to journalArticlepeer-review

Abstract

Practitioners as well as some statistics students often blindly use standard software or algorithms to get maximum likelihood estimator (MLE) without checking the validity of existence of such an estimator. Even in simple situations where data comes from mixtures of Gaussians, global MLE does not exist. This note is intended as a teachers corner, highlighting existential issues related to MLE for mixture models, even when the components are not necessarily Gaussian.

Original languageEnglish (US)
Pages (from-to)1327-1333
Number of pages7
JournalJournal of the Korean Statistical Society
Volume51
Issue number4
DOIs
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Fingerprint

Dive into the research topics of 'A note on maximum likelihood estimation for mixture models'. Together they form a unique fingerprint.

Cite this