Is the cardholder an efficient alarm system to detect credit card incidents?

2020

Statistics in Medicine

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The correct identification of change‐points during ongoing outbreak investigations of infectious diseases is a matter of paramount importance in epidemiology, with major implications for the management of health care resources, public health and, as the COVID‐19 pandemic has shown, social live. Onsets, peaks, and inflexion points are some of them. An onset is the moment when the epidemic starts. A "peak" indicates a moment at which the incorporated values, both before and after, are lower: a maximum. The inflexion points identify moments in which the rate of growth of the incorporation of new cases changes intensity. In this study, after interpreting the concept of elasticity of a random variable in an innovative way, we propose using it as a new simpler tool for anticipating epidemic remission change‐points. In particular, we propose that the "remission point of change" will occur just at the instant when the speed in the accumulation of new cases is lower than the average speed of accumulation of cases up to that moment. This gives stability and robustness to the estimation in the event of possible remission variations. This descriptive measure, which is very easy to calculate and interpret, is revealed as informative and adequate, has the advantage of being distribution‐free and can be estimated in real time, while the data is being collected. We use the 2014‐2016 Western Africa Ebola virus epidemic to demonstrate this new approach. A couple of examples analyzing COVID‐19 data are also included.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Elasticity as a measure for online determination of remission points in ongoing epidemics

2020

Statistics in Medicine

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“…Este trabajo muestra su utilidad en Epidemiología, reforzando el paralelo entre disciplinas. Como concepto económico fue introducido por Alfred Marshall, quien lo tomó prestado de la física, y con esta interpretación se ha extendido muy recientemente al cálculo de probabilidades 15 , a los modelos de supervivencia 16 , a la gestión de riesgos 17 o a la epidemiología 11 . La elasticidad se utiliza para cuantificar la variación experimentada por una variable al cambiar otra.…”

Section: Introductionunclassified

El punto de remisión de una epidemia localizado por su elasticidad. Aplicación a la primera ola del COVID-19 en Chile

Rev. chil. salud pública

2022

Introducción. La detección de cambios en las características de un proceso aleatorio, cono¬cido como el problema del cambio, se ha convertido en un área de investigación estadística en rápido desarrollo. La correcta y rápida detección de los cambios es relevante en muchas situaciones reales, en particular, en Epidemiología. Materiales y Métodos. Como nueva métrica para determinar el momento efectivo de remisión de una epidemia (momento del cambio), se utiliza el concepto de elasticidad de una distribu¬ción de probabilidad, y se aplica a la reciente pandemia COVID-19 en Chile. Resultados. La aplicación evidencia que existe una demora entre el día “pico”o día con el mayor número de casos, con el de “remisión” identificado por la elasticidad. En ese lapso temporal, entre pico y remisión, no deben suavizarse las medidas de control de la epidemia. Se obtiene una diferencia de 20 días entre los puntos de remisión de las series de contagios y muertes. Esta cifra puede interpretarse como una estimación de la supervivencia para los fallecidos durante la primera ola de COVID-19 una vez detectada en ellos la enfermedad. La comparación de los resultados de la aplicación con la de otros países sudamericanos muestra en ellos idéntico resultado que el observado en Chile, si bien con tiempos de demora entre pico y punto de remisión sensiblemente mayores. Discusión. La medida usada en este trabajo es fácil de comunicar, no exige la formulación previa de hipótesis sobre el comportamiento de los datos y puede ser aplicada en tiempo real, tal y como se van conociendo los datos. Estas características de fácil aplicabilidad e interpretación, generando resultados razonables, la hacen atractiva e interesante para el estudio del cambio en series epidemiológicas.

“…The reinterpretation of elasticity in statistical terms and its better comprehension Pavía 2012, 2014) has indirectly expanded the use of (proportional) reserve hazard rates and has directly opened up new areas for their application. In addition to fostering their classical usefulness in survival analysis and stochastic ordering (see, e.g., Navarro et al 2014;Oliveira and Torrado 2015;Balakrishnan et al 2017;Kundu and Ghosh 2017;Hazra et al 2017;Arab and Oliveira 2019;Esna-Ashari et al 2020), they are being applied to new areas, such as epidemiology (Veres-Ferrer and Pavía 2021), risk management in business (Pavía et al 2012;Torrado and Oliviera 2013;Pavía and Veres-Ferrer 2016;Torrado and Navarro 2021), and trading and bidding in markets (Yang et al 2021;Auster and Kellner 2022). Furthermore, these tools are proving their usefulness in the field of medicine (Popović et al 2021), for the characterisation of stochastic distributions Pavía 2017a, 2017b) and as a means for proposing new probability models (Szymkowiak 2019;Kotelo 2019;Anis and De 2020).…”

Section: Introductionmentioning

confidence: 99%

“…This provides valuable information for proper risk management (e.g., economic or health) and enables the situation of the underlying process, a system or an action protocol to be assessed. For instance, an elasticity greater than 1 (or a slightly lower number) in a time stochastic process that measures failures, casualties or deaths is a clear indicator of increasing risk (see, e.g., Pavía and Veres-Ferrer 2016;Veres-Ferrer and Pavía 2021), which reports on the deterioration in the evolution of the variable. From this perspective, therefore, we can affirm that knowing in advance the probabilities of observing elasticities higher than one (or, in general, within a range of interest) of a given random process whose behaviour is synthesised in its probability distribution would constitute a very useful tool for proactive risk management.…”

Section: Introductionmentioning

confidence: 99%

The Elasticity of a Random Variable as a Tool for Measuring and Assessing Risks

2022

Risks

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Elasticity is a very popular concept in economics and physics, recently exported and reinterpreted in the statistical field, where it has given form to the so-called elasticity function. This function has proved to be a very useful tool for quantifying and evaluating risks, with applications in disciplines as varied as public health and financial risk management. In this study, we consider the elasticity function in random terms, defining its probability distribution, which allows us to measure for each stochastic process the probability of finding elastic or inelastic situations (i.e., with elasticities greater or less than 1). This new tool, together with new results on the most notable points of the elasticity function covered in this research, offers a new approach to risk assessment, facilitating proactive risk management. The paper also includes other contributions of interest, such as new results that relate elasticity and inverse hazard functions, the derivation of the functional form of the cumulative distribution function of a probability model with constant elasticity and how the elasticities of functionally dependent variables are related. The interested reader can also find in the paper examples of how elasticity cumulative distribution functions are calculated, and an extensive list of probability models with their associated elasticity functions and distributions.