The total bootstrap median: a robust and efficient estimator of location and scale for small samples

We propose the total bootstrap median (TBM) as a robust and efficient estimator of location and scale for small samples. We demonstrate its performance by estimating the mean and variance of a variety of distributions. We also show that, if the underlying distribution is unknown and there is either no contamination or low to moderate contamination, the TBM provides a better estimate of the mean, in mean square terms, than the sample mean or the sample median. In addition, the TBM is a better estimator of the variance of the underlying distribution than the sample variance or the square of the bias-corrected median absolute deviation from the median estimator. We also show that the TBM is an explicit L-estimator, which allows a direct study of its properties.     

A semi-parametric Bayesian extreme value model using a Dirichlet process mixture of gamma densities

In this paper, we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible for posterior density estimation and posterior inference for high quantiles. The model works well even for small sample sizes and in the absence of prior information. We evaluate the performance of the proposed model through a simulation study. Finally, the proposed model is applied to a real environmental data.     

Kitchen Hygiene in the Spotlight: How Cooking Shows Influence Viewers’ Hygiene Practices

Poor hygiene when handling food is a major cause of foodborne illness. To investigate whether hygiene practices visible in television cooking shows influence viewers’ kitchen hygiene, a study on the adoption of demonstrated hygiene behavior was conducted under controlled, experimental conditions. In a study ostensibly on cooking by following recipes participants (n = 65) were randomly assigned to one of three conditions, in which they watched a cooking video that differed only with regard to the hygiene behavior of the chef. In condition 1, the chef engaged in poor hygiene practices while preparing the dish, in condition 2 the chef's hygiene behavior was exemplary and in condition 3, the chef's hygiene behavior was not visible (control condition). After watching the video, participants were instructed to cook the recipe individually in the fully equipped laboratory kitchen. Cooking sessions were videotaped and experimenters blind to condition coded hygiene lapses committed by participants. The level of kitchen hygiene displayed in the cooking video significantly affected hygiene practices of participants cooking the recipe. Participants who had watched the cooking video with correct hygiene practices committed significantly fewer hygiene lapses than those who had watched the video with poor hygiene practices. From a risk communication perspective, TV cooking shows are well placed to convey knowledge of essential hygiene practices during food preparation to a broad audience. To facilitate behavioral change toward safer food‐handling practices among viewers, visibly performing correct hygiene practices in cooking shows is a promising strategy.     

Chainwise Paired Comparisons

The pairwise comparison technique is a building block of the Analytic Hierarchy Process (AHP), which has been popularly used for multicriteria decision analysis. This paper develops a shortcut technique in which only n paired comparisons forming a closed chain are needed for n decision elements. Together with the development of a simple and intuitive measure of (inconsistency, this technique derives the relative weights of decision elements via easy step-by-step calculations on a spreadsheet format. Its performance has been tested on Saaty's wealth of nations example. It is important to notice that ranking and weights yielded from this alternative technique are identical to Harker's incomplete pairwise comparison solution for the same chain orientation for the example tested.