Statistics Curricula, March Madness Featured in May Issue

John Stufken, Editor, The American Statistician

Book Reviews
Applied Spatial Data Analysis with R
Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gómez-Rubio

The Art of Conjecturing, together with Letter to a Friend on Sets in Court Tennis
Jacob Bernoulli, translated by Edith Dudley Sylla

Bioconductor Case Studies
Florian Hahne, Wolfgang Huber, Robert Gentleman, and Seth Falcon

Business Statistics
Norean R. Sharpe, Richard D. De Veaux, and Paul F. Velleman

Dynamical Processes on Complex Networks
Alain Barrat, Marc Barthélemy, and Alessandro Vespignani

Graph Theory: A Problem Oriented Approach
Daniel A. Marcus

Introduction to Probability and Statistics for Science, Engineering, and Finance
Walter A. Rosenkrantz

The Monty Hall Problem: The Remarkable Story of Math’s Most Contentious Brain Teaser
Jason Rosenhouse

New Drug Development: Design, Methodology, and Analysis
J. Rick Turner

SAS for Data Analysis: Intermediate Statistical Methods
Mervyn G. Marasinghe and William J. Kennedy

The Unfinished Game: Pascal, Fermat, and the Seventeenth-Century Letter That Made the World Modern
Keith Devlin

Wavelet Methods in Statistics with R
G. P. Nason

Applied Survival Analysis: Regression Modeling of Time-to-Event Data (2nd ed.)
David W. Hosmer, Stanley Lemeshow, and Susanne May

The Little SAS Book: A Primer (4th ed.)
Lora D. Delwiche and Susan J. Slaughter

Statistics: Informed Decisions Using Data (3rd ed.)
Michael Sullivan III

The featured article in the May issue is “Computing in the Statistics Curricula,” by Deborah Nolan and Duncan Temple Lang. The authors argue that this is an opportune time to make significant changes in statistics curricula and focus their attention on the integration of computational topics with the teaching of modern statistical methods. Nolan and Lang advocate that computing must be central to the statistics curricula at both the undergraduate and graduate levels and that computational topics must be integrated into traditional statistics courses. The article presents a broad set of computational topics with ideas on how to teach them.

In the General section, Paul Fearnhead and Benjamin M. Taylor extend the madness of the NCAA basketball season in “Calculating Strength of Schedule and Choosing Teams for March Madness.” The authors propose a method to select teams for the NCAA tournament based on their win-loss records after accounting for the different opponents they have played.

David H. Annis, Peter C. Kiessler, Robert Lund, and Tara L. Steuber propose a symmetrized moment estimator for estimation of the one-step-ahead transition probabilities in a reversible Markov chain on a countable state space in “Estimation in Reversible Markov Chains.”

Devan V. Mehrotra, Xiaomin Lu, and Xiaoming Li propose and study two alternatives to the van Elteren test, which uses
within-stratum ranks, for comparing two treatments in a stratified experiment.

In “Three Examples of Accurate Likelihood Inference,” C. Lozada-Can and A. C. Davison demonstrate through illuminating examples that, computationally, it can be relatively straightforward to use higher-order corrections for improving standard likelihood inference.

In the final contribution to this section, Gunnar Taraldsen and Bo Henry Lindqvist present an elementary introduction to a theoretical framework for statistics that includes improper priors, considering both Bayesian and non-Bayesian models.

The Statistical Practice section opens with “A Note on Bayesian Inference After Multiple Imputation,” by Xiang Zhou and Jerome P. Reiter, who consider Bayesian inference on multiply imputed data sets in settings in which posterior distributions of the parameters of interest are not approximately Gaussian. Nicholas T. Longford follows with “Bayesian Decision Making About Small Binomial Rates with Uncertainty About the Prior.”

In “Is the t Confidence Interval: ± t‹(n − 1)s/ Optimal?”, Yijun Zuo proposes a new procedure for constructing a confidence interval for an unknown mean parameter that can outperform not only the classical interval in the title but also bootstrap confidence intervals.

In the History Corner section, Michael Friendly, Pedro Valero-Mora, and Joaquín Ibáñez Ulargui take the reader on an interesting journey featuring Michael Florent van Langren and the first (?) instance of data visualization in “The First (Known) Statistical Graph: Michael Florent van Langren and the ‘Secret’ of Longitude.”