Reports of Conferences,Institutes, and Seminars

This installment of "Serials Spoken Here" covers events that transpired between late September and late October 2008. Reported herein are two Webinars, one on ONIX for Serials, the other on SUSHI, and two conferences: the eighty-fourth annual Meeting of the Potomac Technical Processing Librarians and the New England Library Association's Annual Conference.     

z Test for the significance of the mean of a stable probability distribution with 1<α≤2

The analysis of data using a stable probability distribution with tail parameter α<2 (sometimes called a Pareto–Levy distribution) seems to have been avoided in the past in part because of the lack of a significance test for the mean, even though it appears to be the correct distribution to use for describing returns in the financial markets. A z test for the significance of the mean of a stable distribution with tail parameter 1<α≤2 is defined. Tables are calculated and displayed for the 5% and 1% significance levels for a range of tail and skew parameters α and β. Through the use of maximum likelihood estimates, the test becomes a practical tool even when α and β are not that accurately determined. As an example, the z test is applied to the daily closing prices for the Dow Jones Industrial average from 2 January 1940 to 19 March 2010.     

Consistency of maximum likelihood estimators in a large class of deconvolution models

We consider the maximum likelihood estimator $\hat{F}_n$ of a distribution function in a class of deconvolution models where the known density of the noise variable is of bounded variation. This class of noise densities contains in particular bounded, decreasing densities. The estimator $\hat{F}_n$ is defined, characterized in terms of Fenchel optimality conditions and computed. Under appropriate conditions, various consistency results for $\hat{F}_n$ are derived, including uniform strong consistency. The Canadian Journal of Statistics 41: 98–110; 2013 © 2012 Statistical Society of Canada     

A course in constructing effective displays of data for pharmaceutical research personnel

Interpreting data and communicating effectively through graphs and tables are requisite skills for statisticians and non‐statisticians in the pharmaceutical industry. However, the quality of visual displays of data in the medical and pharmaceutical literature and at scientific conferences is severely lacking. We describe an interactive, workshop‐driven, 2‐day short course that we constructed for pharmaceutical research personnel to learn these skills. The examples in the course and the workshop datasets source from our professional experiences, the scientific literature, and the mass media. During the course, the participants are exposed to and gain hands‐on experience with the principles of visual and graphical perception, design, and construction of both graphic and tabular displays of quantitative and qualitative information. After completing the course, with a critical eye, the participants are able to construct, revise, critique, and interpret graphic and tabular displays according to an extensive set of guidelines. Copyright © 2013 John Wiley & Sons, Ltd.     

Understanding increments in model performance metrics

The area under the receiver operating characteristic curve (AUC) is the most commonly reported measure of discrimination for prediction models with binary outcomes. However, recently it has been criticized for its inability to increase when important risk factors are added to a baseline model with good discrimination. This has led to the claim that the reliance on the AUC as a measure of discrimination may miss important improvements in clinical performance of risk prediction rules derived from a baseline model. In this paper we investigate this claim by relating the AUC to measures of clinical performance based on sensitivity and specificity under the assumption of multivariate normality. The behavior of the AUC is contrasted with that of discrimination slope. We show that unless rules with very good specificity are desired, the change in the AUC does an adequate job as a predictor of the change in measures of clinical performance. However, stronger or more numerous predictors are needed to achieve the same increment in the AUC for baseline models with good versus poor discrimination. When excellent specificity is desired, our results suggest that the discrimination slope might be a better measure of model improvement than AUC. The theoretical results are illustrated using a Framingham Heart Study example of a model for predicting the 10-year incidence of atrial fibrillation.     

Central Limit Theorem approximations for the number of runs in Markov-dependent binary sequences

We consider Markov-dependent binary sequences and study various types of success runs (overlapping, non-overlapping, exact, etc.) by examining additive functionals based on state visits and transitions in an appropriate Markov chain. We establish a multivariate Central Limit Theorem for the number of these types of runs and obtain its covariance matrix by means of the recurrent potential matrix of the Markov chain. Explicit expressions for the covariance matrix are given in the Bernoulli and a simple Markov-dependent case by expressing the recurrent potential matrix in terms of the stationary distribution and the mean transition times in the chain. We also obtain a multivariate Central Limit Theorem for the joint number of non-overlapping runs of various sizes and give its covariance matrix in explicit form for Markov dependent trials.     

Culinary Magazines

Abstract

Marvel Comics, along with rival DC Comics, is one of the two powerhouses of the comic book industry and has been for many decades. The company was founded in 1939 by pulp magazine publisher Martin Goodman. Goodman owned various publishing houses responsible for a variety of pulp titles, including Marvel Science Stories. In 1939, a colleague persuaded Goodman that comic books were the upcoming trend in periodical publishing, so Good-man launched a book called Marvel Comics. Issue no. 1 contained a story by Bill Everett about the Sub-Mariner, as well as the first appearance of the Human Torch. Both characters quickly became leading draws of the Golden Age of comics. Soon after, Goodman hired writer Joe Simon and artist Jack Kirby, who were to become giants in the industry. Simon and Kirby created Captain America, a character who remains a comics icon even today. In those early days, Goodman also hired another future legend: writer Stan Lee, who happened to be Good-man's nephew.

Goodman's comic company was known officially as Timely Comics, Inc., then later as Atlas Publishing. The firm did not change its name to Marvel until the early 1960s. Timely benefitted from the comics boom of the early 1940s, but interest in super-hero stories waned later in the decade. Atlas briefly revived its super-hero line-up in 1954, but the company spent most of the next decade publishing romance, western, horror, and humor comics.1 1. Les Daniels, Marvel: Five Decades of the World's Greatest Comics (NY: Abrams, 1991), pp. 17–23, 26–36, 40–61.¹     

Classes of diffusion-type processes with a sufficient reduction

Classes of processes of the diffusion type permitting a sufficient data reduction are derived. None of these classes are exponential families in the usual sense. For one type of such classes the sufficient statistic equals that of a curved exponential family of diffusion-type processes. For a second type the last observation is sufficient. In particular cases both types of classes are defined by means of a RICCATI equation