Both Sides of the Brain: Strategies for Reinvention for Solo Video Journalists

Technological advances have made it possible for one video journalist to do the work of three. These solo video journalists perform the research and writing functions of a reporter, the field production tasks of a videographer, and the post-production assembly of an editor. Many of these hybrid journalists are veterans of the industry; once single-skilled journalists who have retrained themselves to work alone. However, while technology makes it possible, it takes much more than technical mastery for a professional to make this transition. Not everyone will be able to make the transition. From a series of qualitative interviews with former videographers and reporters, this text examines what factors are required for a successful transition into becoming a solo video journalist, including training, newsroom support, motivation, production competency and personal qualities

Accurate Inference for Repeated Measures in High Dimensions

This paper proposes inferential methods for high-dimensional repeated measures in factorial designs. High-dimensional refers to the situation where the dimension is growing with sample size such that either one could be larger than the other. The most important contribution relates to high-accuracy of the methods in the sense that p-values, for example, are accurate up to the second-order. Second-order accuracy in sample size as well as dimension is achieved by obtaining asymptotic expansion of the distribution of the test statistics, and estimation of the parameters of the approximate distribution with second-order consistency. The methods are presented in a unified and succinct manner that it covers general factorial designs as well as any comparisons among the cell means. Expression for asymptotic powers are derived under two reasonable local alternatives. A simulation study provides evidence for a gain in accuracy and power compared to limiting distribution approximations and other competing methods for high-dimensional repeated measures analysis. The application of the methods are illustrated with a real-data from Electroencephalogram (EEG) study of alcoholic and control subjects

Asymptotics for testing hypothesis in some multivariate variance components model under non-normality

AbstractWe consider the problem of deriving the asymptotic distribution of the three commonly used multivariate test statistics, namely likelihood ratio, Lawley–Hotelling and Bartlett–Nanda–Pillai statistics, for testing hypotheses on the various effects (main, nested or interaction) in multivariate mixed models. We derive the distributions of these statistics, both in the null as well as non-null cases, as the number of levels of one of the main effects (random or fixed) goes to infinity. The robustness of these statistics against departure from normality will be assessed.Essentially, in the asymptotic spirit of this paper, both the hypothesis and error degrees of freedom tend to infinity at a fixed rate. It is intuitively appealing to consider asymptotics of this type because, for example, in random or mixed effects models, the levels of the main random factors are assumed to be a random sample from a large population of levels.For the asymptotic results of this paper to hold, we do not require any distributional assumption on the errors. That means the results can be used in real-life applications where normality assumption is not tenable.As it happens, the asymptotic distributions of the three statistics are normal. The statistics have been found to be asymptotically null robust against the departure from normality in the balanced designs. The expressions for the asymptotic means and variances are fairly simple. That makes the results an attractive alternative to the standard asymptotic results. These statements are favorably supported by the numerical results

High-Dimensional Repeated Measures

Recently, new tests for main and simple treatment effects, time effects, and treatment by time interactions in possibly high-dimensional multigroup repeated-measures designs with unequal covariance matrices have been proposed. Technical details for using more than one between-subject and more than one within-subject factor are presented in this article. Furthermore, application to electroencephalography (EEG) data of a neurological study with two whole-plot factors (diagnosis and sex) and two subplot factors (variable and region) is shown with the R package HRM (high-dimensional repeated measures)

Factors affecting the pathogenicity of fomes lignosus klotzsch

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