We developed a tailored statistical methodology to compare the contamination by chemical elements in soil of Arica, Chile in 2013 and 2014. We study the differences in the consecutive years 2013 and 2014 between arsenic, mercury, chromium, lead, and cadmium occurrence. The complicated aspect of such comparisons is that the heavy-tailed part of the distribution is mixing with the light-tailed distribution of the measurements and annual distributions of each element differ in their variances. We developed a novel sampling methodology based on the generalized Hill approach, which separates extreme values and the central part of the data. We perform a sensitivity analysis of the Fisher-Behrens test and investigate the effect of violation of homoscedasticity depending on the distributions of random samples. Those methods are useful for experimenters when testing for equality of means in sets with homoscedasticity.

We acknowledge the priority on the introduction of the formula of t-lgHill estimator for the positive extreme value index. We provide a novel motivation for this estimator based on ecologically driven dynamical systems. Another motivation is given directly by applying the general t-Hill procedure to log-gamma distribution. We illustrate the good quality of t-lgHill estimator in comparison to classical Hill estimator on the novel data of the concentration of arsenic in drinking water in the rural area of the Arica and Parinacota Region, Chile.

Adverse effects of air pollution on health are a global problem. Chile is no exception due to the increase of urban population and increasing pollution sources. For several years in the winter months in Santiago de Chile, environmental pre-emergency is decreed, which is due to the increase of measurements of contaminants and the risk that this means to health. In order to model the effects of pollution on health we consider a hierarchical Bayesian generalized linear mixed autoregressive model proposed by Ref. [18]. In particular, we apply the model to the number of children with respiratory diseases in the town of Santiago for the period June-August 2011, using the PM2.5 data as covariate obtained by a spatiotemporal pollution model. In order to detect anomalous data, we apply to residuals both robust normality tests together with novel method of probabilities for mild or extreme outliers. We detected significant heterogeneity between stations which offer us better monitoring planning for the future.

This chapter discusses via an interview with Jerzy Filus about his career from his early years in Poland all the way up to today, we will unfold and explore the genesis and development of multivariate pseudonormal distributions, parameter dependence, and finally stochastic dependence in general. As a kind of fusion of the two approaches, the common paper Filus, Filus, and Arnold demonstrated a method of extension of the bivariate normal density where only six parameters were needed; in the Arnold et al. approach, eight parameters were needed as a minimum. In Filus and Filus, the authors gave a reliability motivation for pseudonormal distributions using the parameter dependence paradigm. In this area, Jerzy Filus and coauthors have made substantial recent developments on this topic and propose an interesting new model. In Filus, Filus and Stehlík, a statistical study was conducted related to bivariate pseudoexponential distribution via its survival function, which allows to model other multiple failures.

Statistical challenges of administrative and transaction data : Discussion on the paper by Hand

Summary Administrative data are becoming increasingly important. They are typically the side effect of some operational exercise and are often seen as having significant advantages over alternative sources of data. Although it is true that such data have merits, statisticians should approach the analysis of such data with the same cautious and critical eye as they approach the analysis of data from any other source. The paper identifies some statistical challenges, with the aim of stimulating debate about and improving the analysis of administrative data, and encouraging methodology researchers to explore some of the important statistical problems which arise with such data.