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Border officers experience stressful moments during the working day at the border point at Lisbon airport (Portugal) that can lead to "emotional exhaustion," which is the core component of burnout, and relates with "cognitive fatigue (weariness)" and "physical fatigue." These theoretical concepts cannot be measured (or observed) directly by what are considered latent variables/constructs that are available in Shirom-Melamed Burnout Measure (SMBM). These constructs are, however, operationalized by the questions available in the SMBM, which correspond to observed variables (or manifest variables) expressed in an ordinal scale of seven categories. This questionnaire, reliable and internationally validated, was used in our research and considering the specialized literature, we proposed a hypothetical structural path model that expresses a priori perceptions about the causal relationships between the mentioned latent constructs, where "physical fatigue" is the target construct. A reflective model was estimated based on the sample of primary data and using a Variance-Covariance estimator of Structural Equations Modeling (VB-SEM), the consistent Partial Least Squares (PLSc), that corrects for bias to consistently estimate SEM's with common factors. The results obtained allow a better understanding of the profile and characteristics of Portuguese border officers who work at Lisbon airport, as well as reveal that the latent exogenous construct "emotional exhaustion" has a high positive direct effect on the "cognitive fatigue," and through this latter (mediator construct), an indirect effect on the endogenous construct "physical fatigue." In turn, "cognitive fatigue" has the greatest positive direct effect on "physical fatigue." The biggest difference identified was between the direct effect of "Emotional exhaustion" on "Physical fatigue," which is higher for workers with more years of service, as expected. However, the multigroup analysis (using the nonparametric Permutation test) showed that this difference was not statistically significant. [ABSTRACT FROM AUTHOR]

Multivariate normal distribution is base for many statistical techniques, including ordinary least square inference. Here we show that in order to make research on Internationalization of Companies, more flexible approach is needed, namely partial least squares (PLS). It is a nonparametric technique, used in Structural Equation Modeling (SEM), which makes no distributional assumptions and also may be applied with small sample sizes. In this study we discuss on regularity conditions for PLS from the perspective of semi-continuous covariance which fills the gap in the current studies. The stochastic aspects, especially those related to usage of PLS-SEM, can be well integrated to the topologically grounded regression, where jumps in the covariances can occur. The purpose of the research is to analyze and understand the impact of the Incentive System (IS) for Innovation, within the scope of the National Strategic Reference Framework (QREN) 2014-2020, on the Internationalization of Portuguese Small and Medium Enterprises (SMEs). We study stochastic aspects of theoretical model which aggregates the variables Product Innovation, Marketing Innovation, Organizational Innovation and Working Conditions as determinants of Internationalization of Companies. Data were collected based on a quantitative methodology, through a self-completion questionnaire using the Likert psychometric scale, which registered 120 participants. Organizational Innovation (exogenous latent construct) and Product Innovation have shown a statistically significant indirect effect on the Internationalization of Companies (endogenous latent construct) through Marketing Innovation. The latter has a direct effect on the Internationalization (target construct). However, Working Conditions has the greatest impact on Internationalization, meaning that measures such as increasing wages, decreasing the use of temporary work and precarious work conversion into labor effective relations have a very relevant direct effect on the Internationalization of Portuguese SMEs. [ABSTRACT FROM AUTHOR]

As COVID-19 is spreading, national agencies need to monitor and track several metrics. Since we do not have perfect testing programs on the hand, one needs to develop an advanced sampling strategies for prevalence study, control and management. Here we introduce REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management and control and justify its usage for COVID-19. We show its advantages over classical massive individual testing sampling plans. We also point out how regional and spatial heterogeneity underlines proper sampling. Fundamental importance of adaptive control parameters from emergency health stations and medical frontline is outlined. Since the Northern hemisphere entered Autumn and Winter season (this paper was originally submitted in November 2020), practical illustration from spatial heterogeneity of Chile (Southern hemisphere, which already experienced COVID-19 winter outbreak peak) is underlying the importance of proper regional heterogeneity of sampling plan. We explain the regional heterogeneity by microbiological backgrounds and link it to behavior of Lyapunov exponents. We also discuss screening by antigen tests from the perspective of "on the fly" biomarker validation, i.e., during the screening. [ABSTRACT FROM AUTHOR]

We formulate ill-posedness of inverse problems of estimation and prediction of Coronavirus Disease 2019 (COVID-19) outbreaks from statistical and mathematical perspectives. This is by nature a stochastic problem, since e.g., random perturbation in parameters can cause instability of estimation and prediction. This leaves us with a plenty of possible statistical regularizations, thus generating a plethora of sub-problems. We can mention as examples stability and sensitivity of peak estimation, starting point of exponential growth curve, or estimation of parameters of SIR (Susceptible-Infected-Removed) model. Moreover, each parameter has its specific sensitivity, and naturally, the most sensitive parameter deserves a special attention. E.g., in SIR model, parameter β is more sensitive than parameter γ. In a simple exponential epidemic growth model, parameter b is more sensitive than the parameter a. We also discuss on statistical quality of COVID-19 incidence prediction, where we justify an exponential curve considering the microbial growth in ideal conditions for epidemic. The empirical data from Iowa State, USA, Hubei Province in China, New York State, USA, and Chile justifies an exponential growth curve for initiation of epidemics outbreaks. [ABSTRACT FROM AUTHOR]

We discuss the following problem: Given a set of information criteria for optimal designs, the numerical and computational complexity may drastically differ from one criterion to another. A general novel methodology, called the "FIRCEP" is introduced, and shown to work satisfactorily on a variety of problems relating weighted estimation criterion and Integrated mean square prediction error (IMSPE) prediction criteria in framework of stochastic process. The FIRCEP is shown to be identifying such relationship and providing the exact relations between estimation and prediction for regression problems with correlated errors, without necessity to have known eigenexpansion and truncation methodology. The latter one is the main drawback for automation of complexity reduction algorithms for IMSPE optimization for kernels with unknown eigenexpansion. Thus FIRCEP fills the gap of missing exact method for general kernel satisfying mild regularity conditions in order to develop relation between a class of integrated compound criteria and IMSPE. The exposition proceeds by a series of numerical and real data examples. [ABSTRACT FROM AUTHOR]

We study ARCH/GARCH effects under possible deviation from normality. Since skewness is the principal cause for deviations from normality in many practical applications, e.g. finance, we study in particular skewness. We propose robust tests for normality both for NoVaS and modified NoVaS transformed and original data. Such an approach is not applicable for EGARCH, but applicable for GARCH-GJR models. A novel test procedure is proposed for the skewness in autoregressive conditional volatility models. The power of the tests is investigated with various underlying models. Applications with financial data show the applicability and the capabilities of the proposed testing procedure. [ABSTRACT FROM AUTHOR]