PROPRIETY OF POSTERIORS WITH IMPROPER PRIORS IN HIERARCHICAL LINEAR MIXED MODELS
By Dongchu Sun, R. Tsutakawa, Zhuoqiong He. 2001
73 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/dfb0d915506cbcabfadbdcee741b7ec215a83d1a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Thomas Kneib
- Ludwig Fahrmeir
- Andreas Brezger
- Andrea Hennerfeind
- N. Klein
- Simon N. Wood
- Stefan Lang
- Shawn Ni
Citations in the corpus
- Geoadditive Survival Models (2006, methodology): “The lemma extends theorem 4 of Sun et al. (1999) in two directions.”
- A Bayesian Semiparametric Latent Variable Model for Mixed Responses (2007, unknown): “This is a common approach in Bayesian methodology called data augmentation which was introduced by Tanner and Wong (1987). Albert and Chib (1993) implemented this approach for the estimation of regression parameters and cutpoints for ordinal response.”
- 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.”
- Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression (2016, background): “The results are based on the work of Sun et al. (2001) who derived several upper and lower bounds for the required integrals.”
- Propriety of posteriors in structured additive regression models: Theory and empirical evidence (2009, methodology): “Key words: Bayesian semiparametric regression, Markov random fields, MCMC, penalised splines, propriety of posteriors”
- Noninformative priors and frequentist risks of bayesian estimators of vector-autoregressive models (2003, methodology): “Other noninformative priors, such as Zellner’s (1997) Maximal Data Information Prior (MDIP), can be derived using approaches not discussed in this paper.”