Propriety of posteriors in structured additive regression models: Theory and empirical evidence

By Ludwig Fahrmeir, Thomas Kneib. 2009

47 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7360866d25e60e14b781afafe73d9ecfa3ab41c6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Thomas Kneib
2. N. Klein
3. María Durbán
4. Brian D. Marx
5. Paul H. C. Eilers
6. Stefan Lang
7. Alexander Sohn
8. Ludwig Fahrmeir

In Environmental Engineering

1. Thomas Kneib
2. N. Klein
3. Paul H. C. Eilers
4. María Durbán
5. Brian D. Marx
6. Stefan Lang
7. Alexander Sohn
8. Simon N. Wood

In Statistics and Probability

1. N. Klein
2. Thomas Kneib
3. Paul H. C. Eilers
4. María Durbán
5. Brian D. Marx
6. Stefan Lang
7. Alexander Sohn
8. Simon N. Wood

Citations in the corpus

• Beyond mean regression (2013, background): “Another technical difficulty is the question whether the posterior is actually proper although some of the basis coefficient priors (2.2) are (partially) improper (see Fahrmeir and Kneib, 2009, for some results in this regard).”
• Twenty years of P-splines (2015, methodology): “Bayesian P-splines were proposed by Lang and Brezger (2004), and they were made accessible by appropriate software (Brezger et al.”
• Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression (2016, background): “…|a, θ ∼ IG(a, θ), is a natural, conjugate prior comprising flat priors for the variance and the standard deviation as degenerate special cases (see, e.g., Fahrmeir and Kneib, 2009), there has been considerable debate about the suitability of the inverse gamma distribution especially in the…”
• Bayesian structured additive distributional regression with an application to regional income inequality in Germany (2015, background): “(2001) and Fahrmeir and Kneib (2009) treated this question in exponential family regression while Klein et al.”
• Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data (2015, methodology): “For exponential family regression with similar predictor types, this question has been investigated, for example, in Fahrmeir and Kneib (2009) or Sun, Tsutakawa, and Zhuoqiong (2001) and we will now generalize these results to the framework of GAMLSS.”