Motivational signage increases stair usage on a Hispanic serving institution

Abstract

Objective: Stair climbing is considered a good physical activity. Motivational signage has been successful in promoting stair usage in various settings. This study was to investigate the effects of motivational signage on stair usage in a Hispanic serving institution. Participants: A total of 31,067 pedestrians were observed from February to March 2013. Methods: Stair usage was monitored for 9?h per day each week at phase 1 (baseline), 2 (intervention), and 3 (post-intervention). Results: Overall, participants’ stair usage was higher during phase 2 (49.0%) and phase 3 (48.0%), compared with phase 1 (39.7%). The participants during phase 2 and 3 were more likely to use the stairs compared to participants during phase 1, regardless of floor level (3-story or 4-story building), status (student or staff/faculty), and time of day (7:30–10:29, 10:30–13:29, or 13:30–16:30) (p?<?.001). Conclusion: Motivational signage can effectively encourage more stair usage, and hence promote healthy behavior in a predominantly Hispanic-serving institution.     

Multiple Monte Carlo testing,with applications in spatial point processes

The rank envelope test (Myllymäki et al. in J R Stat Soc B, doi: 10.1111/rssb.12172, 2016) is proposed as a solution to the multiple testing problem for Monte Carlo tests. Three different situations are recognized: (1) a few univariate Monte Carlo tests, (2) a Monte Carlo test with a function as the test statistic, (3) several Monte Carlo tests with functions as test statistics. The rank test has correct (global) type I error in each case and it is accompanied with a p-value and with a graphical interpretation which determines subtests and distances of the used test function(s) which lead to the rejection at the prescribed significance level of the test. Examples of null hypotheses from point process and random set statistics are used to demonstrate the strength of the rank envelope test. The examples include goodness-of-fit test with several test functions, goodness-of-fit test for a group of point patterns, test of dependence of components in a multi-type point pattern, and test of the Boolean assumption for random closed sets. A power comparison to the classical multiple testing procedures is given.     

Should the individual voting records of central bankers be published?

We examine whether the publication of the individual voting records of central-bank council members is socially beneficial when the public is unsure about the efficiency of central bankers and central bankers are angling for re-appointment. We show that publication is initially harmful since it creates a conflict between socially desirable and individually optimal behavior for somewhat less efficient central bankers. However, after re-appointment, losses will be lower when voting records are published since the government can distinguish highly efficient from less efficient central bankers more easily and can make central bankers individually accountable. In our model, the negative effects of voting transparency dominate, and expected overall losses are always larger when voting records are published.     

Finite Sample Properties of the Two-Step Empirical Likelihood Estimator

We investigate the finite sample properties of two-step empirical likelihood (EL) estimators. These estimators are shown to have the same third-order bias properties as EL itself. The Monte Carlo study provides evidence that (i) higher order asymptotics fails to provide a good approximation in the sense that the bias of the two-step EL estimators can be substantial and sensitive to the number of moment restrictions and (ii) the two-step EL estimators may have heavy tails.     

QuickMMCTest: quick multiple Monte Carlo testing

Multiple hypothesis testing is widely used to evaluate scientific studies involving statistical tests. However, for many of these tests, p values are not available and are thus often approximated using Monte Carlo tests such as permutation tests or bootstrap tests. This article presents a simple algorithm based on Thompson Sampling to test multiple hypotheses. It works with arbitrary multiple testing procedures, in particular with step-up and step-down procedures. Its main feature is to sequentially allocate Monte Carlo effort, generating more Monte Carlo samples for tests whose decisions are so far less certain. A simulation study demonstrates that for a low computational effort, the new approach yields a higher power and a higher degree of reproducibility of its results than previously suggested methods.     

Closure properties of classes of multiple testing procedures

Statistical discoveries are often obtained through multiple hypothesis testing. A variety of procedures exists to evaluate multiple hypotheses, for instance the ones of Benjamini–Hochberg, Bonferroni, Holm or Sidak. We are particularly interested in multiple testing procedures with two desired properties: (solely) monotonic and well-behaved procedures. This article investigates to which extent the classes of (monotonic or well-behaved) multiple testing procedures, in particular the subclasses of so-called step-up and step-down procedures, are closed under basic set operations, specifically the union, intersection, difference and the complement of sets of rejected or non-rejected hypotheses. The present article proves two main results: First, taking the union or intersection of arbitrary (monotonic or well-behaved) multiple testing procedures results in new procedures which are monotonic but not well-behaved, whereas the complement or difference generally preserves neither property. Second, the two classes of (solely monotonic or well-behaved) step-up and step-down procedures are closed under taking the union or intersection, but not the complement or difference.     

Environmental markets in the year 2000

This article examines the prospects for marketable emissions permits in the coming decade. We identify both the reasons that politicians are reluctant to embrace marketable emissions permits as a solution to environmental problems and also changes in the political landscape that could lead to the increased use of this approach. Over the next decade, we believe there is reason to be cautiously optimistic. Selective experimentation with market-based approaches is likely to continue; however, we are not optimistic about the prospects for widespread application of this tool. Moreover, applications of this tool will necessarily reflect important political constraints, thus rendering the tool substantially less effective than the textbook applications examined by economists. An important intellectual challenge is to work on the design of market-based approaches that address political concerns, enhance efficiency, and promote innovation, so that it will be easier to meet the environmental challenges that lie ahead.Mr. Hahn is a Resident Scholar at the American Enterprise Institute and Associate Professor of Economics and Public Policy at Carnegie Mellon University. Mr. Noll is a Professor of Economics at Stanford University. Hahn's research was supported by a grant from the Decision, Risk and Management Science Program at the National Science Foundation. Noll's research is supported by a grant from the Smith-Richardson Foundation. The authors would like to thank Kip Viscusi for helpful comments on an earlier draft.     

Re-examining informative prior elicitation through the lens of Markov chain Monte Carlo methods

Summary.  In recent years, advances in Markov chain Monte Carlo techniques have had a major influence on the practice of Bayesian statistics. An interesting but hitherto largely underexplored corollary of this fact is that Markov chain Monte Carlo techniques make it practical to consider broader classes of informative priors than have been used previously. Conjugate priors, long the workhorse of classic methods for eliciting informative priors, have their roots in a time when modern computational methods were unavailable. In the current environment more attractive alternatives are practicable. A reappraisal of these classic approaches is undertaken, and principles for generating modern elicitation methods are described. A new prior elicitation methodology in accord with these principles is then presented.