Regression: Models, Methods and Applications

By Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian D. Marx. 2013

663 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3cdc6332b3a8e3dabf556bece3d3fdda23af0a7b

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In Statistics and Probability

Citations in the corpus

Bayesian Structured Additive Distributional Regression (2013, methodology): “…additive models for location, scale and shape (GAMLSS, [Rigby and Stasinopoulos, 2005]) in which all parameters of a potentially complex response distribution are related to additive regression predictors in the spirit of GAMs [Ruppert et al., 2003, Wood, 2004, 2008, Fahrmeir et al., 2004, 2013].”
Advanced Bayesian Multilevel Modeling with the R Package brms (2017, background): “Example 2: Housing rents In their book about regression modeling, Fahrmeir et al. (2013) use an example about the housing rents in Munich from 1999.”
Bayesian structured additive distributional regression with an application to regional income inequality in Germany (2015, methodology): “We will consider structured additive predictors [Brezger and Lang (2006), Fahrmeir et al. (2013)] where each predictor is determined as an additive combination of various types of functional effects, including nonlinear effects of continuous covariates, spatial effects, random effects or varying…”
Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression (2016, methodology): “The data have been collected as part of nationally representative demographic and health surveys which are freely available at www.measuredhs.com and are described in more detail, e.g. in Fahrmeir et al. (2013).”
GAMLSS: A distributional regression approach (2018, background): “Because an explicit distributional assumption is made for the response variable, they also fall into the category of the ‘distributional regression’ modelling approach, Fahrmeir et al. (2013).”
Bayesian structured additive distributional regression for multivariate responses (2015, background): “In addition, most of these approaches have focused exclusively on linear predictors following the classical framework of generalized linear models (McCullagh and Nelder, 1989; Fahrmeir et al., 2013).”
Modelling Palaeoecological Time Series Using Generalised Additive Models (2018, background): “…with correlation parameter φ. Fahrmeir and Kneib (2008) show that where the stochastic structure of f and 3 approach one another, i.e., where we have a potentially wiggly trend or strong autocorrelation as φ → 1, the two processes can quickly become unidentifiable (see also Fahrmeir et al., 2013).”
Height–diameter equations for larch plantations in northern and northeastern China: a comparison of the mixed-effects, quantile regression and generalized additive models (2016, background): “Apart from independence of the errors among observations and the quantile restriction, there are no further assumptions on the corresponding distribution (Fahrmeir et al., 2013).”