An Overview of Semiparametric Extensions of Finite Mixture Models

TL;DR

This paper reviews recent advances in semiparametric extensions of finite mixture models, highlighting new methodologies, theoretical insights, and open research questions across various scientific fields.

Contribution

It provides a comprehensive overview of recent developments in semiparametric mixture models, focusing on estimation methods, theoretical properties, and future research directions.

Findings

Summarizes recent methodological developments

Discusses theoretical properties of semiparametric models

Identifies open questions and future research areas

Abstract

Finite mixture models have been a very important tool for exploring complex data structures in many scientific areas, for example, economics, epidemiology, finance. In the past decade, semiparametric techniques have been popularly introduced into traditional finite mixture models, and so semiparametric mixture models have experienced exciting development in methodologies, theories and applications. In this article, we provide a selective overview of newly-developed semiparametric mixture models, discuss their estimation methodologies, theoretical properties if applied, and some open questions. Recent developments and some open questions are also discussed.