Finite mixtures of quantile and M-quantile regression models

DOI10.1007/s11222-016-9638-1zbMath1505.62017OpenAlexW2281054877MaRDI QIDQ518274

Nicola Salvati, Maria Giovanna Ranalli, Marco Alfo'

Publication date: 28 March 2017

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11568/778911

zbMATH Keywords

longitudinal datanuisance parametersquantile regressionrobust modelsM-quantile regressionnonparametric MLErandom effect models

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Generalized linear models (logistic models) (62J12)

Related Items

Hidden semi-Markov-switching quantile regression for time series ⋮ Marginal M-quantile regression for multivariate dependent data ⋮ Modelling and estimation of nonlinear quantile regression with clustered data ⋮ Estimation and Testing in M‐quantile Regression with Applications to Small Area Estimation ⋮ Generalised M‐quantile random‐effects model for discrete response: An application to the number of visits to physicians ⋮ Computing finite mixture estimators in the tails ⋮ Robust finite mixture regression for heterogeneous targets ⋮ Semiparametric mixture of regression models under unimodal error distribution ⋮ Quantile regression feature selection and estimation with grouped variables using Huber approximation ⋮ The relationship between longevity and lifespan variation ⋮ Parametric Modeling of Quantile Regression Coefficient Functions With Longitudinal Data

Uses Software

• GLIM

Cites Work

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