Frequentist Properties of Bayesian Methods Applied Bayesian - - PowerPoint PPT Presentation
Frequentist Properties of Bayesian Methods Applied Bayesian Statistics Dr. Earvin Balderama Department of Mathematics & Statistics Loyola University Chicago November 21, 2017 Frequentist Properties 1 Last edited November 20, 2017 by
Applied Bayesian Statistics
Department of Mathematics & Statistics Loyola University Chicago
November 21, 2017
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
So far we have discussed Bayesian methods as being separate from the frequentist approach. There are strengths and weaknesses to both approaches: Sometimes, Bayesian methods do not provide exact frequentist coverage. Frequentist inference violates the likelihood principle. No prescriptive Bayesian approach to model and prior specification. No prescriptive Frequentist approach to small sample problems.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
However, in many cases, methods with frequentist properties are desirable. For example, we may want a method with Type I error control
Bayesian methods are great when the likelihood and prior is fully and correctly specified, but frequentist properties offer protection against misspecification (“all models are wrong, but bad models lead to bad answers”). We can design Bayesian methods to achieve these frequentist properties. In this view, Bayesian methods generate procedures/algorithms for further study. Often Bayesian methods are very competitive with frequentist methods using frequentist criteria.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Little in Little (2011), Statistical Science “To summarize, Bayesian statistics is strong for inference under an assumed model, but relatively weak for the development and assessment of models. Frequentist statistics provides useful tools for model development and assessment, but has weaknesses for inference under an assumed model. If this summary is accepted, then a natural compromise is to use frequentist methods for model development and assessment, and Bayesian methods for inference under a model. This capitalizes on the strengths of both paradigms, and is the essence of the approach known as Calibrated Bayes.”
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Box in Little (2011), Statistical Science “I believe that... sampling theory is needed for exploration and ultimate criticism of the entertained model in the light of the current data, while Bayes’ theory is needed for estimation of parameters conditional on adequacy of the model.”
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Rubin in Little (2011), Statistical Science “The applied statistician should be Bayesian in principle and calibrated to the real world in practice—appropriate frequency calculations help to define such a tie... Frequency calculations are useful for making Bayesian statements scientific, scientific in the sense of capable of being shown wrong by empirical test; here the technique is the calibration of Bayesian probabilities to the frequencies of actual events.”
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
A Bayesian analysis produces a posterior distribution which summarize our uncertainty after observing the data. However, if you have to give a one-number summary as an estimate, you might pick the posterior mean ˆ θB = E(θ |Y) This estimator, ˆ θB, can be evaluated along with MLE or MOM estimators. Is it biased? Is it consistent? How does its MSE compare with the MLE? These are all frequentist properties of the Bayesian estimator.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Similarly, if we have to give an interval estimate, we might use the 95% posterior credible set. Note: In practice, this interval is motivated by the one data set we observed. But we could view this as a procedure for constructing an interval and inspect its frequentist properties. If we analyzed many datasets, each time computing a 95% posterior interval, how many would contain the true value? A Bayes test is to reject H0 if Prob(H0 |Y) < c What are the Type I and Type II errors of this test? Can we pick the threshold c to control Type I error?
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Quote from FDA Because of the inherent flexibility in the design of a Bayesian clinical trial, a thorough evaluation of the operating characteristics should be part of the trial planning. This includes evaluation of: probability of erroneously approving an ineffective or unsafe device (type I error rate), probability of erroneously disapproving a safe and effective device (type II error rate), power (the converse of type II error rate: the probability of appropriately approving a safe and effective device, sample size distribution (and expected sample size), prior probability of claims for the device, and if applicable, probability of stopping at each interim look.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Quote from FDA “Pure” Bayesian approaches to statistics do not necessarily place the same emphasis on the notion of control of type I error as traditional frequentist approaches. There have, however, been some proposals in the literature that Bayesian methods should be “calibrated” to have good frequentist properties (e.g. Rubin, 1984; Box, 1980). In this spirit, as well as in adherence to regulatory practice, FDA recommends you provide the type I and II error rates of your proposed Bayesian analysis plan.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Before studying the frequentist properties of Bayesian estimators and hypothesis tests, we should first determine the “best” Bayesian method. For example, should we take the estimator to be the posterior mean, median, or mode? Defining “best” requires a scoring system; we call this the loss function, L(ˆ θ, θ). Examples: Squared error loss is L(ˆ θ, θ) = (ˆ θ − θ)2 Absolute loss is L(ˆ θ, θ) =
θ − θ
Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
The summary of the posterior that minimizes the expected (posterior) loss is the Bayes rule or Bayes decision rule (not to be confused with Bayes’ Rule). Examples: Squared error loss, L(ˆ θ, θ) = (ˆ θ − θ)2, = ⇒ implies we should use the posterior mean for ˆ θ. Absolute loss, L(ˆ θ, θ) =
θ − θ
= ⇒ implies we should use the posterior median for ˆ θ.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Assume Y1, . . . , Yn ∼ Normal(µ, σ2) Estimator 1: ˆ µ1 = ¯ Y Estimator 2: ˆ µ2 = c ¯ Y, where c =
n n+m
Note: Recall ˆ µ2 is the posterior mean under prior µ ∼ Normal
m
Compute the bias and variance of each estimator
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Compute the mean squared error, MSE = bias2 + variance. Which estimator is preferred?
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Broadly speaking, the following comparisons between Bayes and MLE hold: Bayesian estimators have smaller standard errors because the prior adds information. Bayesian estimators are biased if the prior is not centered on the truth. Depending on this bias/variance trade-off, Bayesian estimators may have smaller MSE than the MLE. If the prior is weak the methods are similar. For any prior that does not depend on the sample size, as n increases the prior is overwhelmed by the likelihood and the posterior approaches the MLE’s sampling distribution.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Assumptions: the usual MLE conditions on the likelihood the prior does not depend on n and puts non-zero probability on the true value θ0 Bayesian CLT For large datasets, the posterior is approximately normal: p(θ |Y) → Normal
, where I is the information matrix. Bayes methods are asymptotically unbiased. Bayes and MLE will be equivalent for large samples!
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
However, the interpretation is different. We can use the Bayesian interpretation, e.g., P(H0 |Y) or P(3.4 < θ < 5.6) How does CLT help? The Bayesian CLT gives a way to approximate the posterior without MCMC (n → ∞). Most still use MCMC with the hope that it better approximates the exact posterior (S → ∞). The CLT is useful for initial values and tuning.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Theoretical studies of Bayesian estimators use the same basic approaches as frequentist methods. Theorems and proofs (of consistency, etc.) are ideal. When the math is intractable, simulation studies are used.
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Frequentist Properties Last edited November 20, 2017 by <ebalderama@luc.edu>
Little in Little (2011), Statistical Science “Philosophy aside, perhaps the main reason why Bayesian methods have flourished in recent years is the development of powerful computational tools, like the Gibbs’ sampler and other Markov Chain Monte Carlo (MCMC)
computationally feasible to carry out the high-dimensional integrations required...” “...Even if frequentists are completely against Bayes, they can apply these Bayesian computational tools with weak prior distributions, and interpret results as approximations to ML, with similar asymptotic properties.”
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