Bayesian exponentially tilted empirical likelihood

By Susanne M. Schennach. 2005

194 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/da3ef96d7b25c98e85d2cb7d485d72009fec04f0

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

In Statistics and Probability

Citations in the corpus

Accompanying document to "Point Estimation with Exponentially Tilted Empirical Likelihood" (2005, methodology): “Finally, given that ETEL’s objective function can be interpreted as a posterior for the parameter θ obtained via a nonparametric Bayesian procedure [58], it may be a more relevant and interesting topic of future research to verify whether a Bayesian Bartlett correction [5], which differs from the us”
Prior Distributions From Pseudo-Likelihoods in the Presence of Nuisance Parameters (2009, background): “Articles that are more specifically related to the validation of (1) have been written by Monahan and Boos (1992), Severini (1999), Lazar (2003) and Schennach (2005).”
Bayesian empirical likelihood for quantile regression (2012, background): “Schennach (2005) and Lancaster and Jun (2010) considered Bayesian exponentially tilted empirical likelihood (ETEL), which can be viewed as a nonparametric Bayesian procedure with noninformative priors on the space of distributions.”
Bayesian Estimation and Comparison of Moment Condition Models (2016, methodology): “L posterior distribution has a Gaussian limiting distribution and that it concentrates on a n−1/2-ball centered at the true value of the parameter. An informal discussion of this behavior is given by Schennach (2005) but without the required assumptions. Theorem 2.1 below provides these assumptions.”
Can Rare Events Explain the Equity Premium Puzzle (2008, methodology): “…(see e.g. Kunitomo and Matsushita (2003), Newey and Smith (2004) and Kitamura (2006)), iii) allow us to perform Bayesian posterior inference (Lazar (2003), Schennach (2005)) that does not rely on asymptotic properties that are less likely to be met, in nite sample, in the presence of rare events.”
Bayesian quantile regression methods (2010, methodology): “For more discussion, see Schennach (2005). be compared with Jeffreys’ 1/n1!n0!. Figure 1 shows the posterior density of the median from a random sample of size n = 50 using a uniform prior.”
Bayesian quantile regression (2006, methodology): “Introduction: Recent work by Schennach(2005) has opened the way to a Bayesian treatment of quantile regression.”