Maximum Binomial Likelihood Method for Multivariate Mixture Data

• 成果类型: Article; Early Access
• 署名作者: Yu, Tao; Qin, Jing; Li, Pengfei
• 署名单位: National University of Singapore; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); University of Waterloo
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2624135
• 发表日期: 2026-04-21
• 关键词: Binomial likelihood; Cumulative distribution function estimation; m-estimator; Multivariate mixture data; Nonparametric mixture model; nonparametric-estimation; SMOOTHED LIKELIHOOD; models; algorithm; Identifiability; components; inference; number

摘要

Multivariate mixture data analysis presents numerous challenges and constitutes a vital area of interest in the fields of statistics and data science. Research into multivariate mixture structures holds relevance across diverse application domains and plays a pivotal role in the advancement of artificial intelligence and machine learning. In this article, we focus on nonparametric estimation techniques for multivariate mixture data. Specifically, we assume a known number of subpopulations and propose a binomial likelihood method, along with an efficient numerical algorithm, to estimate the mixing proportions and cumulative distribution functions of these subpopulations without relying on parametric assumptions. Through extensive numerical experiments, we demonstrate three key advantages of our approach: (a) Our method eliminates the need for tuning parameters. (b) It does not require the assumption of continuous component density functions. (c) Our method consistently delivers stable performance. Under mild regularity conditions, we provide theoretical proofs for the asymptotical properties of our estimators. To illustrate the practical performance of our method, we include a real-data example. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.