Exact tests for correlation and for the slope in simple linear regressions without making assumptions

We present an exact test for whether two random variables that have known bounds on their support are negatively correlated. The alternative hypothesis is that they are not negatively correlated. No assumptions are made on the underlying distributions. We show by example that the Spearman rank correlation test as the competing exact test of correlation in nonparametric settings rests on an additional assumption on the data generating process without which it is not valid as a test for correlation. We then show how to test for the significance of the slope in a linear regression analysis that invovles a single independent variable and where outcomes of the dependent variable belong to a known bounded set.Correlation test, exact hypothesis testing, distribution-free, nonparametric, simple linear regression

Bringing game theory to hypothesis testing: Establishing finite sample bounds on inference

Small sample properties are of fundamental interest when only limited data is avail- able. Exact inference is limited by constraints imposed by speci.c nonrandomized tests and of course also by lack of more data. These e¤ects can be separated as we propose to evaluate a test by comparing its type II error to the minimal type II error among all tests for the given sample. Game theory is used to establish this minimal type II error, the associated randomized test is characterized as part of a Nash equilibrium of a .ctitious game against nature. We use this method to investigate sequential tests for the di¤erence between two means when outcomes are constrained to belong to a given bounded set. Tests of inequality and of noninferiority are included. We .nd that inference in terms of type II error based on a balanced sample cannot be improved by sequential sampling or even by observing counter factual evidence providing there is a reasonable gap between the hypotheses.Exact, distribution-free, nonparametric, independent samples, matched pairs, Z test, unavoidable type II error, noninferiority, LeeX

Distribution-Free Learning

We select among rules for learning which of two actions in a stationary decision problem achieves a higher expected payo¤when payoffs realized by both actions are known in previous instances. Only a bounded set containing all possible payoffs is known. Rules are evaluated using maximum risk with maximin utility, minimax regret, competitive ratio and selection procedures being special cases. A randomized variant of fictitious play attains minimax risk for all risk functions with ex-ante expected payoffs increasing in the number of observations. Fictitious play itself has neither of these two properties. Tight bounds on maximal regret and probability of selecting the best action are included.fictitious play, nonparametric, finite sample, matched pairs, foregone payoffs, minimax risk, ex-ante improving, selection procedure

How to Attain Minimax Risk with Applications to Distribution-Free Nonparametric Estimation and Testing

We show how to a derive exact distribution-free nonparametric results for minimax risk when underlying random variables have known finite bounds and means are the only parameters of interest. Transform the data with a randomized mean preserving transformation into binary data and then apply the solution to minimax risk for the case where random variables are binary valued. This shows that minimax risk is attained by a linear strategy and the the set of binary valued distributions contains a least favorable prior. We apply these results to statistics. All unbiased symmetric non-randomized estimates for a function of the mean of a single sample are presented. We find a most powerful unbiased test for the mean of a single sample. We present tight lower bounds on size, type II error and minimal accuracy in terms of expected length of confidence intervals for a single mean and for the difference between two means. We show how to transform the randomized tests that attain the lower bounds into non-randomized tests that have at most twice the type I and II errors. Relative parameter efficiency can be measured in finite samples, in an example on anti-selfdealing indices relative (parameter) efficiency is 60% as compared to the tight lower bound. Our method can be used to generate distribution-free nonparametric estimates and tests when variance is the only parameter of interest. In particular we present a uniformly consistent estimator of standard deviation together with an upper bound on expected quadratic loss. We use our estimate to measure income inequality.exact, distribution-free, nonparametric inference, finite sample theory

Why Imitate, and if so, How? Exploring a Model of Social Evolution

In consectutive rounds, each agent in a finite population chooses an action, is randomly matched, obtains a payoff and then observes the performance of another agent. An agent determines future behavior based on the information she receives from the present round. She chooses among the behavioral rules that increase expected payoffs in any specifications of the matching scenario. The rule that outperforms all other such rules specifies to imitate the action of an agent that performed better with probability proportional to how much better she performed. The evolution of a large population in which each agent uses this rule can be approximated in the short run by the replicator dynamics.Random matching, learning, imitation, replicator dynamics

Robust Monopoly Pricing

We consider a robust version of the classic problem of optimal monopoly pricing with incomplete information. In the robust version of the problem the seller only knows that demand will be in a neighborhood of a given model distribution. We characterize the optimal pricing policy under two distinct, but related, decision criteria with multiple priors: (i) maximin expected utility and (ii) minimax expected regret. While the classic monopoly policy and the maximin criterion yield a single deterministic price, minimax regret always prescribes a random pricing policy, or equivalently, a multi-item menu policy. The resulting optimal pricing policy under either criterion is robust to the model uncertainty. Finally we derive distinct implications of how a monopolist responds to an increase in ambiguity under each criterion.Monopoly, Optimal pricing, Robustness, Multiple priors, Regret

Finite Sample Exact tests for Linear

We introduce tests for finite sample multivariate linear regressions with heteroskedastic errors that have mean zero. We assume bounds on endoge- nous variables but do not make additional assumptions on errors. The tests are exact, i.e., they have guaranteed type I error probabilities. We provide bounds on probability of type II errors, and apply the tests to empirical data.

Minimax regret and strategic uncertainty

This paper introduces a new solution concept, a minimax regret equilibrium, which allows for the possibility that players are uncertain about the rationality and conjectures of their opponents. We provide several applications of our concept. In particular, we consider pricesetting environments and show that optimal pricing policy follows a non-degenerate distribution. The induced price dispersion is consistent with experimental and empirical observations (Baye and Morgan (2004)).Minimax regret, rationality, conjectures, price dispersion, auction

Robust Monopoly Pricing

We consider a robust version of the classic problem of optimal monopoly pricing with incomplete information. In the robust version, the seller faces model uncertainty and only knows that the true demand distribution is in the neighborhood of a given model distribution. We characterize the optimal pricing policy under two distinct, but related, decision criteria with multiple priors: (i) maximin expected utility and (ii) minimax expected regret. The resulting optimal pricing policy under either criterion yields a robust policy to the model uncertainty. While the classic monopoly policy and the maximin criterion yield a single deterministic price, minimax regret always prescribes a random pricing policy, or equivalently, a multi-item menu policy. Distinct implications of how a monopolist responds to an increase in uncertainty emerge under the two criteria.Monopoly, Optimal pricing, Robustness, Multiple priors, Regret