The Birnbaum-Saunders (BS) distribution is a model that frequently appears in the statistical literature and has proved to be very versatile and efficient across a wide range of applications. However, despite the growing interest in the study of this distribution and the development of many articles, few of them have considered data with a dependency structure. To fill this gap, we introduce a new class of time series models based on the BS distribution, which allows modeling of positive and asymmetric data that have an autoregressive structure. We call these BS autoregressive moving average (BISARMA) models. Also included is a thorough study of theoretical properties of the proposed methodology and of practical issues, such as maximum likelihood parameter estimation, diagnostic analytics, and prediction. The performance of the proposed methodology is evaluated using Monte Carlo simulations. An analysis of real-world data is performed using the methodology to show its potential for applications. The numerical results report the excellent performance of the BISARMA model, indicating that the BS distribution is a good modeling choice when dealing with time series data with positive support and asymmetrically distributed. Hence, it can be a valuable addition to the toolkit of applied statisticians and data scientists.
Environmental agencies are interested in relating mortality to pollutants and possible environmental contributors such as temperature. The Gaussianity assumption is often violated when modeling this relationship due to asymmetry and then other regression models should be considered. The class of Birnbaum–Saunders models, especially their regression formulations, has received considerable attention in the statistical literature. These models have been applied successfully in different areas with an emphasis on engineering, environment, and medicine. A common simplification of these models is that statistical dependence is often not considered. In this paper, we propose and derive a time-dependent model based on a reparameterized Birnbaum–Saunders (RBS) asymmetric distribution that allows us to analyze data in terms of a time-varying conditional mean. In particular, it is a dynamic class of autoregressive moving average (ARMA) models with regressors and a conditional RBS distribution (RBSARMAX). By means of a Monte Carlo simulation study, the statistical performance of the new methodology is assessed, showing good results. The asymmetric RBSARMAX structure is applied to the modeling of mortality as a function of pollution and temperature over time with sensor-related data. This modeling provides strong evidence that the new ARMA formulation is a good alternative for dealing with temporal data, particularly related to mortality with regressors of environmental temperature and pollution.
ResumoA técnica denominada de classificação baseada em objetos, proposta por Baatz & Shäpe (2000), trata-se de uma abordagem de processamento de imagens em que a unidade primitiva é o objeto, composto de vários pixels. Softwares proprietários como o eCognition® e de open source como o InterIMAGE realizam o processamento de imagens baseadas em objetos considerando o alto grau de relações mútuas e ações em diferentes escalas, como informações de contexto, estrutura semântica e hierárquica. O objetivo principal da pesquisa foi demonstrar e avaliar resultados da integração de sistemas open source com o sistema de classificação denominado InterIMAGE. Os sistemas utilizados nessa pesquisa foram o sistema de gerenciamento de banco de dados objeto-relacional PostgreSQL/PostGIS Raster, biblioteca TerraLib, pacote computacional de sistema de informações geográficas QGIS e linguagem de programação C++. Foi utilizada uma imagem do satélite GeoEye-1 de 2013 de uma área urbana do município de Goianésia no estado de Goiás. Foi desenvolvida uma interface (API -Application Programming Interface) no sistema InterIMAGE para realizar a segmentação multiresolução em ambiente de banco de dados espaciais. A segmentação processada utilizou-se da API com a imagem armazenada no PostgreSQL e em disco rígido, enquanto a classificação foi efetuada somente no InterIMAGE. O índice Kappa foi utilizado para indicação da acurácia dos resultados alcançados na classificação, utilizando-se os parâmetros da segmentação da API, obtendo-se um valor de 0,412. As regras da árvore de decisão devem ser modificadas para realização de novos experimentos visando verificar a influência no processamento da classificação no InterIMAGE. Apesar da ocorrência de algumas confusões temáticas no processo de classificação, demonstrou-se a viabilidade de continuação de desenvolvimento de aplicações de código aberto para o InterIMAGE.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.