Adaptive Spline Smoothing in Non-Gaussian Regression Models

1990 ◽  
Vol 85 (411) ◽  
pp. 801-807 ◽  
Author(s):  
Chong Gu
Keyword(s):  
Regression Models ◽  
Spline Smoothing ◽  
Non Gaussian ◽  
Gaussian Regression

scholarly journals A joint regression modeling framework for analyzing bivariate binary data in R

2017 ◽  
Vol 5 (1) ◽  
pp. 268-294 ◽  
Author(s):  
Giampiero Marra ◽  
Rosalba Radice
Keyword(s):  
Binary Data ◽  
Regression Models ◽  
Joint Modeling ◽  
Data Sets ◽  
Modeling Framework ◽  
Flexible Joint ◽  
Sampling Schemes ◽  
Multivariate Response ◽  
Interpretable Models ◽  
Non Gaussian

Abstract We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets.

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