On the maximization of likelihoods belonging to the exponential family using a Levenberg–Marquardt approach

M. Giordan*, F. Vaggi, R. Wehrens

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

The Levenberg–Marquardt algorithm is a flexible iterative procedure used to solve non-linear least-squares problems. In this work, we study how a class of possible adaptations of this procedure can be used to solve maximum-likelihood problems when the underlying distributions are in the exponential family. We formally demonstrate a local convergence property and discuss a possible implementation of the penalization involved in this class of algorithms. Applications to real and simulated compositional data show the stability and efficiency of this approach
Original languageEnglish
Pages (from-to)895-907
JournalJournal of Statistical Computation and Simulation
Volume87
Issue number5
DOIs
Publication statusPublished - 2017

Fingerprint Dive into the research topics of 'On the maximization of likelihoods belonging to the exponential family using a Levenberg–Marquardt approach'. Together they form a unique fingerprint.

Cite this