Zero-inflated compound Poisson distributions in integer-valued GARCH models

In this paper we introduce a wide class of integer-valued stochastic processes that allows to take into consideration, simultaneously, relevant characteristics observed in count data namely zero inflation, overdispersion and conditional heteroscedasticity. This class includes, in particular, the compound Poisson, the zero-inflated Poisson and the zero-inflated negative binomial INGARCH models, recently proposed in literature. The main probabilistic analysis of this class of processes is here developed. Precisely, first- and second-order stationarity conditions are derived, the autocorrelation function is deduced and the strict stationarity is established in a large subclass. We also analyse in a particular model the existence of higher-order moments and deduce the explicit form for the first four cumulants, as well as its skewness and kurtosis.     

Radiological Risk Assessment by Convergence Methodology Model in RDD Scenarios

A radiological dispersal device (RDD) is a simple weapon capable of causing human harm, environmental contamination, disruption, area denial, and economic cost. It can affect small, large, or long areas depending on atmospheric stability. The risk of developing a radio‐induced cancer depends on exposure, and an effective response depends upon available timely guidance. This article proposes and demonstrates a convergence of three different capabilities to assess risk and support rapid safe resource efficient response. The three capabilities that are integrated are Hotspot for dispersion, RERF for epidemiological risk, and RESRAD‐RDD for response guidance. The combined methodology supports decisions on risk reduction and resource allocation through work schedules, the designation and composition of response teams, and siting for operations. In the illustrative RDD scenario, the contamination area for sheltering, evacuation, and long‐term public concern was greatest for calm atmospheric conditions, whilst close‐quarter responders faced highest dose rates for neutral atmospheric conditions. Generally, the risks to women responders were found to be significantly greater than for men, and the risks to 20‐year‐old responders were three times that of their 60‐year‐old counterparts for similar exposure.     

Enhancing standard performance practices with DEA

Data envelopment analysis (DEA) can be interpreted as a natural generalization of elemental practices for efficiency and performance evaluation used in diverse areas, some of which have evolved independently of each other and of the traditional efficiency framework of Charnes et al. [Measuring the efficiency of decision making units. European Journal of Operational Research 1978;2(6):429–44]. The paper aims to show that some standard “real-world” procedures employed by practitioners in the fields of retailing, finance, and social science can be interpreted as unconscious applications of DEA, and that such analyses could be enhanced by the explicit use of DEA. A supplementary aim is to demonstrate that DEA can be derived from concrete examples and first principles. The choice of the examples covers the two typical returns to scale environments of constant returns to scale (CRS) and variable returns to scale (VRS). Motivating and deriving DEA as a generalization of established performance evaluation practices will help to explain and disseminate this methodology to practitioners and to those who find their methods familiar.     

A strange tale

We are going through such challenging times that dealing with reality is sometimes easier through fiction. Therefore, we tell the tale of a faraway land where poor women — mostly black — who make a living as domestic servants are particularly impacted by the COVID‐19 pandemic. Their abject bodies are part of a form of necropolitics that separates those who must live from those who can die. Their bodies do not matter, as they are perceived as mere working tools. And what sounds like a strange tale is the true reality of millions of women during the pandemic, in a land that is not so far away as it seems.