Comments on “On the weighted mixed Liu-type estimator under unbiased stochastic restrictions”

We make some comments about the paper of Yildiz (2017 Yildiz, N. 2017. On the weighted mixed Liu-type estimator under unbiased stochastic restrictions. Communications in Statistics Simulation and Computation 46 (9):7238–48. do?:10.1080/03610918.2016.1235189.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) and correct the theorems in that paper.     

Uncertain Risk Assessment and Management: Case Studies of the Application of the Precautionary Principle in Portugal

This study intends to clarify how the precautionary principle (PP) has been interpreted and applied by the courts in Portugal in the analysis of conflicts associated with uncertain and serious potential risks to human health and the environment. It also aims to contribute to the debate of when and how to apply precautionary measures. To this end, recent court cases in the areas of waste incineration, high-voltage power lines, as well as dam and wind farm construction were considered. The degree of consistency in the courts’ decisions and their reasons in the different judicial bodies was analyzed with the support of a theoretical framework based on three attributes: the level of seriousness of potential hazards, level of evidence required, and the severity of precautionary actions taken. Different positions among courts were observed, with contradictory arguments in the same case or in similar cases. A greater propensity for favorable decisions in the acceptance of restraining orders was verified in the courts of lower instances, where human health could be threatened. However, the decisions of the Supreme Administrative Court, which were always unfavorable to the restraining orders, seem to reflect the priority given to national economic and political interests over local or regional environmental interests. They may also reflect the Supreme Court's reluctancy to apply the PP in the absence of a firm legally binding PP in national legislation. To address this situation, more explicit legal requirements and criteria for the analysis of uncertain risks and the weighting of interests by area of activity are needed.     

On bootstrap testing inference in cure rate models

Survival models deal with the time until the occurrence of an event of interest. However, in some situations the event may not occur in part of the studied population. The fraction of the population that will never experience the event of interest is generally called cure rate. Models that consider this fact (cure rate models) have been extensively studied in the literature. Hypothesis testing on the parameters of these models can be performed based on likelihood ratio, gradient, score or Wald statistics. Critical values of these tests are obtained through approximations that are valid in large samples and may result in size distortion in small or moderate sample sizes. In this sense, this paper proposes bootstrap corrections to the four mentioned tests and bootstrap Bartlett correction for the likelihood ratio statistic in the Weibull promotion time model. Besides, we present an algorithm for bootstrap resampling when the data presents cure fraction and right censoring time (random and non-informative). Simulation studies are conducted to compare the finite sample performances of the corrected tests. The numerical evidence favours the corrected tests we propose. We also present an application in an actual data set.     

Stockpiling supplies for disaster response: an experimental analysis of prepositioning biases

Abstract

Several relief organisations preposition supplies in preparation for disaster response. We show how the structure of prepositioning decisions resemble newsvendor decisions and how common newsvendor biases could affect prepositioning performance. To explore prepositioning biases, we run a behavioural experiment of a prepositioning problem with 20 practitioners from the humanitarian sector. Their task was to choose prepositioning quantities under different cost conditions and for supplies with different degrees of criticality. Results show that participants pull orders away from optimum (pull-to-centre) and follow the prior beneficiary demand realisation (demand chasing) regardless of cost and criticality conditions. However, the estimation of behavioural models reveals patterns hidden in averages, showing that participants weigh undersupply costs more for urgent supplies and urgency moderates demand chasing. A follow-up validation experiment manipulates the salience of urgent supplies by bundling them with non-urgent supplies in a portfolio. Results show that the portfolio strengthens these patterns, increasing the availability of urgent supplies. We offer practice implications of these findings for prepositioning activities and theory implications for inventory experiments in general.     

The use of military bunkers by the European badger and red fox in Western Europe

Urban Ecosystems - Intensification of agriculture and land consolidation policy induced the removal of most natural features from farmland, reducing the habitat of many wild species. Abandoned WWII...     

A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact

We developed a simulation model for quantifying the spatio‐temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio‐temporal exposure‐hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (b iological risk a ssessment with R ) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.     

Estimation of a nonlinear discriminant function from a mixture of two GEV distributions

In this paper, the identifiability of finite mixture of generalized extreme value (GEV) distributions is proved. Next, a procedure for finding maximum likelihood estimates (MLEs) of the parameters of a finite mixture of two generalized extreme value (MGEV) distributions is presented by using classified and unclassified observations. Then, a nonlinear discriminant function for a mixture of two GEV distributions is derived and the performance of the corresponding estimated discriminant function is investigated through a series of simulation experiments. Finally, the methodology is applied to real data.     

Human Development Index (HDI) Rank-Order Variability

In accordance with the increasing demand for information, indices are created and national and global rankings made to represent and through which to understand and build policy related to complex situations, processes and trajectories. Different indices for a single concept are also created that have advantages or disadvantages over one another or to overcome certain calculation problems. As one such, the Human Development Index (HDI) presently lists countries according to four different criteria, and remains at the heart of democratic and humanitarian recovery efforts. This type of indicator is taken as a function of past performances, with high performances being the extreme values at positively skewed distributions. Thus, the variability of each unit’s repeated measures is regarded as the result of efforts made between the measurement time points (in the HDI case, of a country to promote development). However, it is assumed that the variability of the units is not homogenous. Here, it is shown that in the HDI case, high performance units show relatively low variability, whereas the middle and middle-low performance units show a high variability. Cluster analysis and Friedman test have been used to determine the characteristics of ordered country rankings. The variability of rank-order should also be taken into account besides the location on the list by clustering the countries according to HDI.     

Zero-truncated compound Poisson integer-valued GARCH models for time series

Starting from the compound Poisson INGARCH models, we introduce in this paper a new family of integer-valued models suitable to describe count data without zeros that we name zero-truncated CP-INGARCH processes. For such class of models, a probabilistic study concerning moments existence, stationarity and ergodicity is developed. The conditional quasi-maximum likelihood method is introduced to consistently estimate the parameters of a wide zero-truncated compound Poisson subclass of models. The conditional maximum likelihood method is also used to estimate the parameters of ZTCP-INGARCH processes associated with well-specified conditional laws. A simulation study that compares some of those estimators and illustrates their finite distance behaviour as well as a real-data application conclude the paper.     

Bayesian significance test for discriminating between survival distributions

An evaluation of FBST, Fully Bayesian Significance Test, restricted to survival models is the main objective of the present paper. A Survival distribution should be chosen among the tree celebrated ones, lognormal, gamma, and Weibull. For this discrimination, a linear mixture of the three distributions is an important tool: the FBST is used to test the hypotheses defined on the mixture weights space. Another feature of the paper is that all three distributions are reparametrized in that all the six parameters are written as functions of the mean and the variance of the population been studied. Some numerical results from simulations with some right-censored data are considered.