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This has been discussed in my paper (published only on the internet) "On an Example of Larry Wasserman" ([http://sims.princeton.edu/yftp/WassermanExmpl1)] and in a blog exchange between me, Wasserman, Robins, and some other commenters on Wasserman's blog: [http://normaldeviate.wordpress.com/2012/09/02/robins-and-wasserman-respond-to-a-nobel-prize-winner-continued-a-counterexample-to-bayesian-inference/2]

The short answer is that Wasserman (and Robins) generate paradoxes by suggesting that priors in high dimensional spaces "must" have characteristics that imply either that the parameter of interest is known a priori with near certainty or that a clearly relevant problem (selection bias) is known with near certainty not to be present. In fact, sensible priors would not have these characteristics. I'm in the process of writing a summary blog post to draw this together. There is an excellent 2007 paper, showing sensible Bayesian approaches to the examples Wasserman and Ritov consider, by Hameling and Toussaint: “Bayesian estimators for Robins-Ritov’s problem” [http://eprints.pascal-network.org/archive/00003871/01/harmeling-toussaint-07-ritov.pdf]3]

This has been discussed in my paper (published only on the internet) "On an Example of Larry Wasserman" (http://sims.princeton.edu/yftp/WassermanExmpl) and in a blog exchange between me, Wasserman, Robins, and some other commenters on Wasserman's blog: http://normaldeviate.wordpress.com/2012/09/02/robins-and-wasserman-respond-to-a-nobel-prize-winner-continued-a-counterexample-to-bayesian-inference/

The short answer is that Wasserman (and Robins) generate paradoxes by suggesting that priors in high dimensional spaces "must" have characteristics that imply either that the parameter of interest is known a priori with near certainty or that a clearly relevant problem (selection bias) is known with near certainty not to be present. In fact, sensible priors would not have these characteristics. I'm in the process of writing a summary blog post to draw this together. There is an excellent 2007 paper, showing sensible Bayesian approaches to the examples Wasserman and Ritov consider, by Hameling and Toussaint: “Bayesian estimators for Robins-Ritov’s problem” [http://eprints.pascal-network.org/archive/00003871/01/harmeling-toussaint-07-ritov.pdf]

This has been discussed in my paper (published only on the internet) "On an Example of Larry Wasserman" [1] and in a blog exchange between me, Wasserman, Robins, and some other commenters on Wasserman's blog: [2]

The short answer is that Wasserman (and Robins) generate paradoxes by suggesting that priors in high dimensional spaces "must" have characteristics that imply either that the parameter of interest is known a priori with near certainty or that a clearly relevant problem (selection bias) is known with near certainty not to be present. In fact, sensible priors would not have these characteristics. I'm in the process of writing a summary blog post to draw this together. There is an excellent 2007 paper, showing sensible Bayesian approaches to the examples Wasserman and Ritov consider, by Hameling and Toussaint: “Bayesian estimators for Robins-Ritov’s problem” [3]

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source | link

This has been discussed in my paper (published only on the internet) "On an Example of Larry Wasserman" (http://sims.princeton.edu/yftp/WassermanExmpl) and in a blog exchange between me, Wasserman, Robins, and some other commenters on Wasserman's blog: http://normaldeviate.wordpress.com/2012/09/02/robins-and-wasserman-respond-to-a-nobel-prize-winner-continued-a-counterexample-to-bayesian-inference/

The short answer is that Wasserman (and Robins) generate paradoxes by suggesting that priors in high dimensional spaces "must" have characteristics that imply either that the parameter of interest is known a priori with near certainty or that a clearly relevant problem (selection bias) is known with near certainty not to be present. In fact, sensible priors would not have these characteristics. I'm in the process of writing a summary blog post to draw this together. There is an excellent 2007 paper, showing sensible Bayesian approaches to the examples Wasserman and Ritov consider, by Hameling and Toussaint: “Bayesian estimators for Robins-Ritov’s problem” [http://eprints.pascal-network.org/archive/00003871/01/harmeling-toussaint-07-ritov.pdf]