Adaptive priors based on splines with random knots

DOI10.1214/14-BA879zbMath1327.62130arXiv1303.3365OpenAlexW2963600204MaRDI QIDQ899059

Eduard Belitser, Paulo Serra

Publication date: 21 December 2015

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1303.3365

zbMATH Keywords

splinerandom knotsadaptive priorBayesian non-parametricoptimal contraction rate

Mathematics Subject Classification ID

Bayesian inference (62F15) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items

Bayesian Model Selection in Additive Partial Linear Models Via Locally Adaptive Splines ⋮ Posterior consistency for the spectral density of non‐Gaussian stationary time series ⋮ Posterior contraction rates of density derivative estimation ⋮ Rate-optimal Bayesian intensity smoothing for inhomogeneous Poisson processes ⋮ Bayesian sieve methods: approximation rates and adaptive posterior contraction rates ⋮ Bayesian Analysis of the Proportional Hazards Model with Time‐Varying Coefficients ⋮ Geometrically designed, variable knot regression splines

Cites Work

• Unnamed Item
• Analysis of high dimensional multivariate stochastic volatility models
• Probabilistic forecasts of volatility and its risk premia
• Markov chain Monte Carlo methods for stochastic volatility models.
• Post-'87 crash fears in the S\&P 500 futures option market
• Transform Analysis and Asset Pricing for Affine Jump-diffusions

This page was built for publication: Adaptive priors based on splines with random knots