Spline-based sieve maximum likelihood estimation in the partly linear model under monotonicity constraints

DOI10.1016/j.jmva.2010.07.002zbMath1198.62032OpenAlexW2071071788MaRDI QIDQ604367

Minggen Lu

Publication date: 10 November 2010

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.07.002

zbMATH Keywords

Monte Carloempirical processmonotone \(B\)-splinesgeneralized Rosen algorithmmaximal likelihood method

Mathematics Subject Classification ID

Related Items (11)

Robust estimation in partially linear regression models with monotonicity constraints ⋮ Double penalized semi-parametric signed-rank regression with adaptive LASSO ⋮ Robust estimators in a generalized partly linear regression model under monotony constraints ⋮ M-estimation for the partially linear regression model under monotonic constraints ⋮ Finite mixture of varying coefficient model: estimation and component selection ⋮ On computation of semiparametric maximum likelihood estimators with shape constraints ⋮ Spline-based sieve estimation in monotone constrained varying-coefficient partially linear EV model ⋮ Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data ⋮ Parameter estimation of partial linear model under monotonicity constraints with censored data ⋮ Empirical Likelihood Inference of the Partial Linear Isotonic Errors-in-variables Regression Models with Missing Data ⋮ Spline estimation of generalised monotonic regression

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