The Duality of Parameters and the Duality of Probability

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Submission Summary
Under any inferential paradigm, statistical inference is connected to the logic of probability. Well-known debates among these various paradigms emerge from conflicting views on the notion of probability. One dominant view understands the logic of probability as a representation of variability (frequentism), and another prominent view understands probability as a measurement of belief (Bayesianism). The first camp generally describes model parameters as fixed values, whereas the second camp views parameters as random. Just as calibration (Reid and Cox 2015, “On Some Principles of Statistical Inference,” International Statistical Review 83(2), 293-308)--the behavior of a procedure under hypothetical repetition--bypasses the need for different versions of probability, I propose that an inferential approach based on confidence distributions (CD), which I will explain, bypasses the analogous conflicting perspectives on parameters. Frequentist inference is connected to the logic of probability through the notion of empirical randomness. Sample estimates are useful only insofar as one has a sense of the extent to which the estimator may vary from one random sample to another. The bounds of a confidence interval are thus particular observations of a random variable, where the randomness is inherited by the random sampling of the data. For example, 95% confidence intervals for parameter θ can be calculated for any random sample from a Normal N(θ, 1) distribution. With repeated sampling, approximately 95% of these intervals are guaranteed to yield an interval covering the fixed value of θ. Bayesian inference produces a probability distribution for the different values of a particular parameter. However, the quality of this distribution is difficult to assess without invoking an appeal to the notion of repeated performance. For data observed from a N(θ, 1) distribution to generate a credible interval for θ requires an assumption about the plausibility of different possible values of θ, that is, one must assume a prior. However, depending on the context - is θ the recovery time for a newly created drug? or is θ the recovery time for a new version of an older drug? - there may or may not be an informed choice for the prior. Without appealing to the long-run performance of the interval, how is one to judge a 95% credible interval [a, b] versus another 95% interval [a', b'] based on the same data but a different prior? In contrast to a posterior distribution, a CD is not a probabilistic statement about the parameter, rather it is a data-dependent estimate for a fixed parameter for which a particular behavioral property holds. The Normal distribution itself, centered around the observed average of the data (e.g. average recovery times), can be a CD for θ. It can give any level of confidence. Such estimators can be derived through Bayesian or frequentist inductive procedures, and any CD, regardless of how it is obtained, guarantees performance of the estimator under replication for a fixed target, while simultaneously producing a random estimate for the possible values of θ.
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Swarthmore College

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