Pseudo-likelihood approach for parameter estimation in univariate normal mixture models

Raul Kangro, Kristi Kuljus () and Jüri Lember
Additional contact information
Raul Kangro: University of Tartu
Kristi Kuljus: University of Tartu
Jüri Lember: University of Tartu

Statistical Papers, 2025, vol. 66, issue 1, No 22, 29 pages

Abstract: Abstract This article proposes a new approach for parameter estimation in univariate normal mixture distributions. The proposed method combines distance-based parameter estimation with maximum likelihood estimation and is therefore referred to as a pseudo-likelihood approach. In this pseudo-likelihood approach, the weights of the mixture components can be considered as functions of mixture component means and standard deviations. The proposed method has two main advantages in comparison to the traditional likelihood approach: (1) the pseudo-likelihood is always bounded, thus the global maximum exists; (2) since the mixture weights are functions of component means and standard deviations, the number of estimated parameters is reduced which may play an important role in models with many mixture components. We present several simulation examples to demonstrate the behaviour of the proposed method in different situations in comparison to other parameter estimation methods. It is interesting to observe that in some models, where it is more difficult to separate the components and the number of components is large, the pseudo-likelihood method beats the maximum likelihood method even for large sample sizes.

Keywords: Normal mixture distributions; Pseudo-likelihood; Maximum likelihood estimation; Distance-based estimation; Kernel density (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-024-01642-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01642-1

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-024-01642-1

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().