Maximum multinomial likelihood estimation in compound mixture model with application to malaria study

DOI10.1080/10485252.2021.1898609zbMath1472.62155OpenAlexW3137041779MaRDI QIDQ5012333

Kun Liang, Zhaoyang Tian, Peng-Fei Li

Publication date: 1 September 2021

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2021.1898609

zbMATH Keywords

EM-algorithmempirical likelihoodbinomial likelihoodclinical malariaparasite level

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05)

Related Items (1)

Maximum profile binomial likelihood estimation for the semiparametric Box-Cox power transformation model

Cites Work

• Unnamed Item
• Unnamed Item
• Combining isotonic regression and EM algorithm to predict genetic risk under monotonicity constraint
• On the convergence properties of the EM algorithm
• Biased sampling, over-identified parameter problems and beyond
• An Empirical Distribution Function for Sampling with Incomplete Information
• Bayesian Analysis of Two-Component Mixture Distributions Applied to Estimating Malaria Attributable Fractions
• Estimating the Malaria Attributable Fever Fraction Accounting for Parasites Being Killed by Fever and Measurement Error
• A Semiparametric Two‐Component “Compound” Mixture Model and Its Application to Estimating Malaria Attributable Fractions

This page was built for publication: Maximum multinomial likelihood estimation in compound mixture model with application to malaria study