Nonlinear estimation methods applied To a problem of expected inflation

Indexación: ScieloEn este trabajo se describe y analiza, luego de tener la ecuación que relaciona la dinámica de las expectativas de la tasa de interés real y de la inflación, el filtro extendido de Kalman. Asimismo, se realiza la estimación de la inflación exante para una serie de datos preestablecida. Se efectúa una comparación con el método de estimación de horizonte móvil utilizado en situaciones cuando producto de las incertidumbres paramétricas del modelo éste se torna no lineal. La aplicación de estos métodos a datos reales permite concluir que las estimaciones efectuadas a través del método de horizonte móvil, combinado a un algoritmo heurístico de optimización logran los mejores resultados. Palabras clave: Filtrado de Kalman, método MHSE, inflación, modelos econométricos. ABSTRACT This work describes and analyses the Extended Kalman Filter with regard to an equation that relates the dynamic of the expected real interest rates and inflation. An estimation of the exante inflation for a preestablished data set is carried out. This is compared with the same calculation using a moving horizon estimation method for situations that are non lineal due to parametric uncertainties of the model. From the application of these methods to real data, it can be concluded that the estimations based on the moving horizon method, combined with a heuristic optimization algorithm, yield better results. Keywords: Kalman Filter, MHSE method, inflation, econometric models

MÉTODOS DE ESTIMACIÓN NO LINEAL APLICADOS AL PROBLEMA DE EXPECTATIVAS DE INFLACIÓN NONLINEAR ESTIMATION METHODS APPLIED TO A PROBLEM OF EXPECTED INFLATION

En este trabajo se describe y analiza, luego de tener la ecuación que relaciona la dinámica de las expectativas de la tasa de interés real y de la inflación, el filtro extendido de Kalman. Asimismo, se realiza la estimación de la inflación exante para una serie de datos preestablecida. Se efectúa una comparación con el método de estimación de horizonte móvil utilizado en situaciones cuando producto de las incertidumbres paramétricas del modelo éste se torna no lineal. La aplicación de estos métodos a datos reales permite concluir que las estimaciones efectuadas a través del método de horizonte móvil, combinado a un algoritmo heurístico de optimización logran los mejores resultados.This work describes and analyses the Extended Kalman Filter with regard to an equation that relates the dynamic of the expected real interest rates and inflation. An estimation of the exante inflation for a preestablished data set is carried out. This is compared with the same calculation using a moving horizon estimation method for situations that are non lineal due to parametric uncertainties of the model. From the application of these methods to real data, it can be concluded that the estimations based on the moving horizon method, combined with a heuristic optimization algorithm, yield better results

Dose Estimation by Geant4-Based Simulations for Cone-Beam CT Applications: A Systematic Review

The last two decades have witnessed increasing use of X-ray imaging and, hence, the exposure of humans to potentially harmful ionizing radiation. Computed tomography accounts for the largest portion of medically-related X-ray exposure. Accurate knowledge of ionizing radiation dose from Cone-Beam CT (CBCT) imaging is of great importance to estimate radiation risks and justification of imaging exposures. This work aimed to review the published evidence on CBCT dose estimation by focusing on studies that employ Geant4-based toolkits to estimate radiation dosage. A systematic review based on a scientometrics approach was conducted retrospectively, from January 2021, for a comprehensive overview of the trend, thematic focus, and scientific production in this topic. The search was conducted using WOS, PubMed, and Scopus databases, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. In total, 93 unique papers were found, of which only 34 met the inclusion criteria. We opine that the findings of this study provides a basis to develop accurate simulations of CBCT equipment for optimizing the trade-off between clinical benefit and radiation risk

Strategies to Automatically Derive a Process Model from a Configurable Process Model Based on Event Data

Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms and a greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework and tested in both business-like event logs as recorded in a higher educational enterprise resource planning system and a real case scenario involving a set of Dutch municipalities