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     

A Simulation Study of Predicting Flush Date

For Canada's boreal forest region, the accurate modelling of the timing of the appearance of aspen leaves is important to forest fire management, as it signifies the end of the spring fire season that occurs after snowmelt. This article compares two methods, a midpoint rule and a conditional expectation method used to estimate the true flush date for interval-censored data from a large set of fire-weather stations in Alberta, Canada. The conditional expectation method uses the interval censored kernel density estimator of Braun et al. (2005 Braun , J. , Duchesne , T. , Stafford , J. E. ( 2005 ). Local likelihood density estimation for interval censored data . Canadian Journal of Statistics 33 : 39 – 60 .[Crossref], [Web of Science ®] , [Google Scholar]). The methods are compared via simulation, where true flush dates were generated from a normal distribution and then converted into intervals by adding and subtracting exponential random variables. The simulation parameters were estimated from the data set and several scenarios were considered. The study reveals that the conditional expectation method is never worse than the midpoint method, and that there is a significant advantage to this method when the intervals are large. An illustration of the methodology applied to the Alberta data set is also provided.     

A Multivariate Synthetic Control Chart for Monitoring the Process Mean Vector of Skewed Populations Using Weighted Standard Deviations

This article proposes a multivariate synthetic control chart for skewed populations based on the weighted standard deviation method. The proposed chart incorporates the weighted standard deviation method into the standard multivariate synthetic control chart. The standard multivariate synthetic chart consists of the Hotelling's T ² chart and the conforming run length chart. The weighted standard deviation method adjusts the variance–covariance matrix of the quality characteristics and approximates the probability density function using several multivariate normal distributions. The proposed chart reduces to the standard multivariate synthetic chart when the underlying distribution is symmetric. In general, the simulation results show that the proposed chart performs better than the existing multivariate charts for skewed populations and the standard T ² chart, in terms of false alarm rates as well as moderate and large mean shift detection rates based on the various degrees of skewnesses.     

Systematic Effects of Capital Service Price Definition on Perceptions of Input Substitution

Earlier attempts at reconciling disparate substitution elasticity estimates examined differences in separability hypotheses, data bases, and estimation techniques, as well as methods employed to construct capital service prices. Although these studies showed that differences in elasticity estimates between two or three studies may be attributable to the aforementioned features of the econometric models, they have been unable to demonstrate this link statistically and establish the existence of systematic relationships between features of the econometric models and the perception of production technologies generated by those models. Using sectoral data covering the entire production side of the U.S. economy, we estimate 34 production models for alternative definitions of the capital service price. We employ substitution elasticities calculated from these models as dependent variables in the statistical search for systematic relationships between features of the econometric models and perceptions of the sectoral technology as characterized by the elasticities. Statistically significant systematic effects are found between the monotonicity and concavity properties of the cost functions and service price–technical change specifications as well as between substitution elasticities.     

Estimating the Structure of Agricultural Investment: A Stochastic- Coefficients Approach

Two approaches, a flexibie-accelerator model and a stochastic-coefficients alternative, are used to estimate the structure of aggregate agricultural investment. Structural estimates of the adjustment rates for each model differ. The stochastic-coefficients model, however, performs better in out-of-sample forecasting.     

Powers of Discrete Goodness-of-Fit Test Statistics for a Uniform Null Against a Selection of Alternative Distributions

The comparative powers of six discrete goodness-of-fit test statistics for a uniform null distribution against a variety of fully specified alternative distributions are discussed. The results suggest that the test statistics based on the empirical distribution function for ordinal data (Kolmogorov–Smirnov, Cramér–von Mises, and Anderson–Darling) are generally more powerful for trend alternative distributions. The test statistics for nominal (Pearson's chi-square and the nominal Kolmogorov–Smirnov) and circular data (Watson's test statistic) are shown to be generally more powerful for the investigated triangular (∨), flat (or platykurtic type), sharp (or leptokurtic type), and bimodal alternative distributions.     

A New Family of Nonparametric Quantile Estimators

A new core methodology for creating nonparametric L-quantile estimators is introduced and three new quantile L-estimators (SV1 _p , SV2 _p , and SV3 _p ) are constructed using the new methodology. Monte Carlo simulation was used in order to investigate the performance of the new estimators for small and large samples under normal distribution and a variety of light and heavy-tailed symmetric and asymmetric distributions. The new estimators outperform, in most of the cases studied, the Harrell–Davis quantile estimator and the weighted average at X _([np]) quantile estimator.