A penalized likelihood estimation approach to semiparametric sample selection binary response modeling

DOI10.1214/13-EJS814zbMath1337.62170MaRDI QIDQ1951164

Rosalba Radice, Giampiero Marra

Publication date: 29 May 2013

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

Full work available at URL: https://projecteuclid.org/euclid.ejs/1369148599

zbMATH Keywords

bivariate probitnon-random sample selectionpenalized regression splinebinary responses

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Sampling theory, sample surveys (62D05)

Related Items (9)

On \(p\)-values for semiparametric bivariate probit models ⋮ Semi-parametric copula sample selection models for count responses ⋮ Copula based generalized additive models for location, scale and shape with non-random sample selection ⋮ Multivariate effect priors in bivariate semiparametric recursive Gaussian models ⋮ A joint regression modeling framework for analyzing bivariate binary data in \(\mathsf{R}\) ⋮ A penalized likelihood estimation approach to semiparametric sample selection binary response modeling ⋮ Semi-parametric bivariate polychotomous ordinal regression ⋮ Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models ⋮ Correcting for sample selection bias in Bayesian distributional regression models

Uses Software

• R
• Stata
• gss
• SemiPar
• SemiParBIVProbit
• gamair

Cites Work

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• Smoothing spline ANOVA models

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