Asymptotic inference for mixture models by using data‐dependent priors

By Larry A. Wasserman. 2000

144 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bc7c1d4d77bb8e6befbf9c3a32f2aaf52ae8b417

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Citations in the corpus

Robust priors in nonlinear panel data models (2007, background): “12This result is in a similar spirit to one in Wasserman (2000), which shows that for certain mixture models data-dependent priors are the only priors that produce intervals with second-order frequentist coverage.”
Bayesian Model Averaging: A Tutorial (2016, methodology): “Using the BIC approximation to the integrated likelihood defines an inherent prior on all of the regression parameters, as outlined in Kass and Wasserman (1995). This prior is a sensible one to take in the absence of substantial prior information; it is a normal distribution centered at the null hyp”
Modelling Heterogeneity With and Without the Dirichlet Process (2001, methodology): “This model has been discussed by Wasserman (2000), in the context of non-informative priors for mixture models with ®xed k.”
Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, unknown): “Finally, data-based priors can be derived based on a variety of methods including maximum likelihood (see, Berger, 2006; Brown, 2008; Candel & Winkens, 2003; van der Linden, 2008) or sample statistics (see, e.g., Darnieder, 2011; Raftery, 1996; Rich-ardson & Green, 1997; Wasserman, 2000).”
Model selection in high dimensions: a quadratic‐risk‐based approach (2006, background): “As mentioned earlier, a comparison of the non-parametric risk and the parametric risk gives a global assessment of model quality.”
Is Bayes Posterior just Quick and Dirty Confidence (2011, background): “Of course, data dependent priors have a long but rather infrequent presence, for example, Box and Cox (1964), Wasserman (2000) and Fraser et al. (2010b).”
Objective Testing Procedures in Linear Models: Calibration of the p‐values (2006, unknown): “Objective procedures for choosing Pr(M0), 0( 0) and 1( 1) is becoming very popular (Berger, 2000; Clyde & George, 2000; Kim & Sun, 2000; Wasserman, 2000; Berger et al., 2001; Sweeting, 2001; Casella & Moreno, 2005, 2006).”
Default priors for Bayesian and frequentist inference (2010, methodology): “Data-dependent priors have been discussed in the literature in various contexts, such as Box and Cox (1964) and Wasserman (2000).”
Bayesian Nonparametric Calibration and Combination of Predictive Distributions (2015, background): “Wasserman (2000) for a discussion on the use of improper prior in mixture modelling.”
Asymptotics and the theory of inference (2003, background): “shows that the result cannot be true for nonparametric settings, and Wasserman (2000) provides insight into why this is the case.”
K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation? (2015, background): “…might also be situations where the researcher explicitly wants to assign state-dependent information to a specific state, e.g. in outlier detection analysis or when working with financial data typically having infrequent states of high volatility and low persistence, see e.g. also (Wasserman 2000).”
THE FORMAL DEFINITION OF REFERENCE PRIORS (2009, background): “(There are data-dependent objective priors: see Wasserman [43].”