Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data

By Ludwig Fahrmeir, Stefan Lang. 2001

153 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/28c07b061fe6962245617e42e7764e59f763e54e

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Citations in the corpus

Bayesian structured additive distributional regression for multivariate responses (2015, background): “For instance, Fahrmeir and Lang (2001) proposed multicategorical regression models in the spirit of generalized additive models (Hastie and Tibshirani, 1990; Ruppert et al., 2003; Wood, 2006; Fahrmeir et al., 2013) from a Bayesian point of view.”
Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression (2016, methodology): “In the context of Bayesian inference in structured additive regression as it has originally been proposed in Fahrmeir and Lang (2001) or Fahrmeir et al. (2004) for exponential families, prior elicitation is in particular an issue when it comes to choosing hyperpriors for the smoothing variances.”
Bayesian P-Splines (2004, methodology): “° Finally, we intend to extend the approach to non-Gaussian errors, for example, by using similar sampling schemes as in Albert and Chib (1993) or Fahrmeir and Lang (2001b) for categorical probit models or as in Fahrmeir and Lang (2001a) for generalized additive models.”
BayesX: Analyzing Bayesian Structural Additive Regression Models (2005, methodology): “For continuous covariates BayesX supports random walk priors (Fahrmeir and Lang 2001a) and Bayesian P-splines (Lang and Brezger 2004).”
PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE-TIME DATA: A BAYESIAN PERSPECTIVE (2004, methodology): “Based on previous own work (Fahrmeir and Lang (2001a), Fahrmeir and Lang (2001b), Lang and Brezger (2003) and Brezger and Lang (2003)), non-linear effects of continuous covariates as well as smooth time trends are modelled through Bayesian versions of penalized splines (P-splines), introduced in a…”
Monotonic Regression Based on Bayesian P–Splines (2008, methodology): “…approaches make use of adaptive knot selection (e.g., Smith and Kohn 1996; Denison, Mallick and Smith 1998; Biller 2000; Di Matteo, Genovese and Kass 2001; Biller and Fahrmeir 2001; Hansen and Kooperberg 2002) or smoothness priors (Hastie and Tibshirani 2000; Fahrmeir and Lang 2001a,b).”
Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data (2015, background): “As shown in the supplement of Fahrmeir and Lang (2001), the unstructured and the structured spatial effect can generally not be separated and are often estimated with bias.”
High dimensional structured additive regression models: Bayesian regularization, smoothing and predictive performance (2011, methodology): “following Albert & Chib (1993) for linear predictors and Fahrmeir & Lang (2001b) for”
A Bayesian Semiparametric Latent Variable Model for Mixed Responses (2007, unknown): “Different definitions of neighbourhood are given in Besag, York and Mollie (1991) and can be modelled by a more general prior distribution including equation (7) as a special case as described in Fahrmeir and Lang (2001b). The entire prior distribution follows as p(β) ∝ exp(−β′Kβ/(2κ2)) with the d-d”
Bayesian varying-coefficient models using adaptive regression splines (2001, methodology): “The Metropolis–Hastings algorithm (the most general MCMC technique, see, for example, Gilks et al., 1996) ensures that the transition kernels of the Markov chain converge to the target distribution. Samples are generated through an appropriate proposal density qðy; y0Þ, from which a new value y0 can”
Generalized structured additive regression based on Bayesian P-splines (2006, methodology): “P-splines usually perform better in terms of bias and mean squared error than the simple random walk priors used in Fahrmeir and Lang (2001a) and Fahrmeir and Lang (2001b).”