A robust class of homoscedastic nonlinear regression models

DOI10.1080/00949655.2019.1635598OpenAlexW2955307130WikidataQ127581842 ScholiaQ127581842MaRDI QIDQ5107490

Darren Wraith, Zahra Barkhordar, Mohsen Maleki, Zahra Khodadadi

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2019.1635598

zbMATH Keywords

maximum likelihood estimatestwo-piece distributionsnonlinear regression modelECME-algorithmscale mixtures of normal family

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (6)

Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models ⋮ A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models ⋮ Robust clustering of COVID-19 cases across U.S. counties using mixtures of asymmetric time series models with time varying and freely indexed covariates ⋮ Robust mixture regression modeling based on two-piece scale mixtures of normal distributions ⋮ Leptokurtic and platykurtic class of robust symmetrical and asymmetrical time series models ⋮ Heteroscedastic nonlinear regression models using asymmetric and heavy tailed two-piece distributions

Uses Software

• R

Cites Work

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