On posterior consistency in nonparametric regression problems

By Taeryon Choi, Mark J. Schervish. 2007

136 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ee79e4db0f74c43c7f5c9b9ff7565b83e2f82777

How researchers see this paper

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

In Artificial Intelligence

1. Bernhard Schölkopf
2. Seth R. Flaxman
3. Alex Smola
4. Stefan Bauer
5. Joachim M. Buhmann
6. Daniel B. Neill
7. Jian Qing Shi
8. Andreas Krause

In Statistics and Probability

1. Seth R. Flaxman
2. Daniel B. Neill
3. Jian Qing Shi
4. Ravi Ramamoorthi
5. Alex Smola
6. Stefan Bauer
7. Andreas Krause
8. Joachim M. Buhmann

Citations in the corpus

• Remarks on consistency of posterior distributions (2008, methodology): “Regarding either posterior consistency or rate of convergence of posterior distributions, this model has been investigated by Shen and Wasserman [26], Walker [33] and Choi and Schervish [8].”
• Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs (2018, background): “Even though the exact choice might not be too important for consistency guarantees in GP regression (Choi and Schervish, 2007), this choice directly influences the amount of observations that are needed for reasonable performance.”
• A Gaussian process regression approach to a single-index model (2011, methodology): “A simplified proof will be given in Appendix A, and further technical details can also be found in Choi and Schervish (2007).”
• On the Frequentist Properties of Bayesian Nonparametric Methods (2016, unknown): “…estimation for i.i.d data, see (Schwartz, 1965, Barron et al., 1999, Lijoi et al., 2005) for generic results, regression function estimation (Choi and Schervish, 2007, Ghosal and Roy, 2006), or in models with non independent data (Ghosal and Tang, 2006, Rousseau et al., 2012, Vernet, 2014)…”
• Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference (2015, methodology): “Based on the preceding general framework, we propose the use of GP regression, which is almost surely consistent assuming Gaussian errors [Choi and Schervish 2007] with good rates of convergence [Van Der Vaart and Van Zanten 2011], and the HSIC for testing for independence.”