Stochastic model specification search for Gaussian and partial non-Gaussian state space models

By Sylvia Frühwirth-Schnatter, Helga Wagner. 2010

245 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/81de6f0e3a290a9aef93f5e533a766b421a4ffbe

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In Artificial Intelligence

Citations in the corpus

Sparse Bayesian time-varying covariance estimation in many dimensions (2016, background): “The signs of the loadings have not been identified so that a multimodal posterior distribution hints at a “significant” loading whereas a unimodal posterior hints at a zero loading, see also Frühwirth-Schnatter and Wagner (2010). It stands out that only very little shrinkage is induced by the standa”
Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression (2016, methodology): “…there has been considerable debate about the suitability of the inverse gamma distribution especially in the context of hierarchical random effects models (see, for example, Gelman, 2005, 2006; Hodges, 2013) or for overfitting models as demonstrated in Frühwirth-Schnatter and Wagner (2010, 2011).”
Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility (2018, unknown): “This provides an alternative to the stochastic model specification search approach of Frühwirth-Schnatter and Wagner (2010), which has been extended to TVP-VARs in Belmonte et al. (2014) and Eisenstat, Chan, and Strachan (2016).”
Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models (2014, methodology): “Note that this specification differs from the commonly employed conjugate Inverse- Gamma prior σ2 ∼ G−1 (c0, C0) and is motivated by Frühwirth-Schnatter & Wagner (2010), who equivalently stipulate the prior for ± √ σ2 to follow a centered normal distribution, i.e. ± √ σ2 ∼ N (0, Bσ).”
Time-varying sparsity in dynamic regression models (2014, unknown): “Groen et al. (2009) introduced a latent variable which indicates whether a variable is included in or excluded from the model.”