Structured Additive Regression for Categorical Space–Time Data: A Mixed Model Approach

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P-spline ANOVA-type interaction models for spatio-temporal smoothing (2011, background): “They extend the geoadditive models proposed by Kammann and Wand (2003) or assume a smooth function to model non-linear time effects (MacNab and Dean, 2001; Fahrmeir et al., 2004; Kneib and Fahrmeir, 2006).”
Smooth Transition from Fixed Effects to Mixed Effects Models in Multi-level regression Models (2008, methodology): “In applied statistics, semiparametric Bayesian approaches are often used in combination with (penalized) splines, series or MCMC (random field) estimators, see Fahrmeir, Kneib, and Lang (2004), Kneib and Fahrmeir (2006) and Fahrmeir and Lang (2001) among others.”
Smoothing Parameter and Model Selection for General Smooth Models (2015, unknown): “ven −∞= α 0 <α 1,...<αR = ∞we have that y= rif a latent variable u= µ+ ǫis such that αr−1 <u≤αr, which occurs with probability Pr(Y = r) = F(αr −µ)−F(αr−1 −µ) where F is the c.d.f. of ǫ. See Kneib and Fahrmeir (2006) and (Fahrmeir et al., 2013, section 6.3.1) for a particularly clear exposi”
Geoadditive Survival Models (2006, methodology): “Within a discrete-time setting, spatial survival data from this study were analyzed by Crook, Knorr-Held, and Hemingway (2003). Fahrmeir, Lang, Wolff, and Bender (2003) investigated the impact of small-area labor market regions and other covariates, including calendar time, age, and unemployment ben”