Generalized linear models (GLMs) outline a wide class of regression models where the effect of the explanatory variables on the mean of the response variable is modelled throughout the link function. The choice of the link function is typically overlooked in applications and the canonical link is commonly used. The estimation of GLMs with unspecified link function is discussed, where the linearity assumption between the link and the linear predictor is relaxed and the unspecified relationship is modelled flexibly by means of P-splines. An estimating algorithm is presented, alternating estimation of two working GLMs up to convergence. The method is applied to the analysis of quit behavior of production workers where the logit, probit and clo...
AbstractWe analyze in a regression setting the link between a scalar response and a functional predi...
Extended linear models form a very general framework for sta-tistical modeling. Many practically imp...
We study generalized additive partial linear models, proposing the use of polynomial spline smoothin...
Generalized linear models (GLMs) outline a wide class of regression models where the effect of the e...
Generalized linear models (GLM) allow for a wide range of statistical models for regression data. In...
Nonparametric methods for the estimation of the link function in generalized linear models are able ...
With the release of Stata 7, the glm command for fitting generalized linear models underwent a subst...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
In statistics, linear modelling techniques are widely used methods to explain one variable by others...
In this paper we extend the GeDS methodology, recently developed by Kaishev et al. [18] for the Norm...
In this article we consider robust generalized estimating equations for the analysis of semiparametr...
This paper presents a fully Bayesian approach to regression splines with automatic knot selection in...
In the longitudinal data analysis we integrate flexible linear predictor link function and high-corr...
I describe a command that simultaneously solves the extended estimating equations estimator for para...
The generalised linear model is a flexible predictive model for observational data that is widely us...
AbstractWe analyze in a regression setting the link between a scalar response and a functional predi...
Extended linear models form a very general framework for sta-tistical modeling. Many practically imp...
We study generalized additive partial linear models, proposing the use of polynomial spline smoothin...
Generalized linear models (GLMs) outline a wide class of regression models where the effect of the e...
Generalized linear models (GLM) allow for a wide range of statistical models for regression data. In...
Nonparametric methods for the estimation of the link function in generalized linear models are able ...
With the release of Stata 7, the glm command for fitting generalized linear models underwent a subst...
UnrestrictedGeneralized linear models (GLMs) are introduced by Nelder and Wedderburn. As an extensio...
In statistics, linear modelling techniques are widely used methods to explain one variable by others...
In this paper we extend the GeDS methodology, recently developed by Kaishev et al. [18] for the Norm...
In this article we consider robust generalized estimating equations for the analysis of semiparametr...
This paper presents a fully Bayesian approach to regression splines with automatic knot selection in...
In the longitudinal data analysis we integrate flexible linear predictor link function and high-corr...
I describe a command that simultaneously solves the extended estimating equations estimator for para...
The generalised linear model is a flexible predictive model for observational data that is widely us...
AbstractWe analyze in a regression setting the link between a scalar response and a functional predi...
Extended linear models form a very general framework for sta-tistical modeling. Many practically imp...
We study generalized additive partial linear models, proposing the use of polynomial spline smoothin...