A joint regression modeling framework for analyzing bivariate binary data in \(\mathsf{R}\)

DOI10.1515/demo-2017-0016OpenAlexW2772889291MaRDI QIDQ1697000

Rosalba Radice, Giampiero Marra

Publication date: 15 February 2018

Published in: Dependence Modeling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/demo-2017-0016

zbMATH Keywords

copulaselection biasconfoundingbinary datajoint modelsimultaneous parameter estimation\(\mathsf{R}\)penalized smoother

Mathematics Subject Classification ID

Multivariate analysis (62H99) General nonlinear regression (62J02)

Related Items (2)

Estimating the impact of medical care usage on work absenteeism by a trivariate probit model with two binary endogenous explanatory variables ⋮ GJRM

Uses Software

• R
• CRAN
• VGAM
• Stata
• SAS
• SemiPar
• mgcv
• GAMLSS
• SemiParBIVProbit
• sampleSelection
• gamair
• SAS/STAT
• mvProbit
• SAS/ETS
• mvprobit

Cites Work

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• Semiparametric Regression
• Effect of Insurance on Mortality in an HIV-Positive Population in Care
• Instrumental Variable Estimators for Binary Outcomes
• On p-values for smooth components of an extended generalized additive model
• Understanding Relationships Using Copulas
• Generalized Additive Models for Location, Scale and Shape
• Longitudinal Data Analysis

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