A Methodology for Data-Driven Decision-Making in the Monitoring of Particulate Matter Environmental Contamination in Santiago of Chile

Cavieres, María Fernanda; Leiva, Víctor; Marchant, Carolina; Rojas, Fernando

doi:10.1007/398_2020_41

Cited by 11 publications

(14 citation statements)

References 36 publications

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“…is a block diagonal matrix with elements x i . To determine the ML estimates of the model parameters formulated in (2), we must equate the elements of the gradient vector stated in (7) to zero and, in this manner, obtain a homogeneous system of equations. Note that, for the log-BS-t distribution, as ν → ∞, one has −2ζ i approaching one, for all i = 1, .…”

Section: Bivariate Gbs Log-linear Modelsmentioning

confidence: 99%

“…The BFGS algorithm is implemented in the R software by the function optim. Note that the gradient vector and Hessian matrix are analytically computed from the expressions defined in (7) and (8), respectively, and not numerically from the optim function. In addition, warm start/initial value selection for the iterative procedure is obtained from: (i) the ordinary least square estimate…”

Section: Bivariate Gbs Log-linear Modelsmentioning

confidence: 99%

“…Thus, during the months of autumn and winter, pollutants stay trapped in the Santiago valley, which produces atmospheric contamination in the city. Due to meteorological and topographical factors, there exists an accumulation of PM and gaseous pollutants during winter, and an increase in solar radiation is observed during the summer which favors photochemical reactions [6,7].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…Such pollutants and their high levels vary according to meteorological and geographical fluctuations, which depend on source and type of emission changes. As a result of this variation, atmospheric contaminant levels are treated as random variables with positive support, that can be modeled by a probability distribution skewed to the right [7]. Furthermore, the relationship between meteorological variables and PM has been analyzed around the world [8] and the most prominent variables in literature are used as covariates for the predictive models proposed in the present study.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model

et al. 2021

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Improving air quality is an important environmental challenge of our time. Chile currently has one of the most stable and emerging economies in Latin America, where human impact on natural resources and air quality does not go unperceived. Santiago, the capital of Chile, is one of the cities in which particulate matter (PM) levels exceed national and international limits. Its location and climate cause critical conditions for human health when interaction with anthropogenic emissions is present. In this paper, we propose a predictive model based on bivariate regression to estimate PM levels, related to PM2.5 and PM10, simultaneously. Birnbaum-Saunders distributions are used in the joint modeling of real-world PM2.5 and PM10 data by considering as covariates some relevant meteorological variables employed in similar studies. The Mahalanobis distance is utilized to assess bivariate outliers and to detect suitability of the distributional assumption. In addition, we use the local influence technique for analyzing the impact of a perturbation on the overall estimation of model parameters. In the predictions, we check the categorization for the observed and predicted cases of the model according to the primary air quality regulations for PM.

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Section: Bivariate Gbs Log-linear Modelsmentioning

confidence: 99%

Section: Bivariate Gbs Log-linear Modelsmentioning

confidence: 99%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model

et al. 2021

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“…651‐663) , and Leiva 3 . Although the BS distribution has its origins in physics and engineering, it has also received interest in several other areas, including, but no limited to, business, earth sciences, industry and medicine; for details, see References 4‐20. The study of the BS distribution has received growing interest and a considerable amount of work is available; see the recent publications by Leiva, 3 Balakrishnan and Kundu, 21 and references therein, which summarize most of the works to the date.…”

Section: Introductionmentioning

confidence: 99%

Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data

Sánchez

Leiva

Galea

et al. 2020

Appl Stoch Models Bus & Ind

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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 the BS distribution, quantile regression modeling has not been considered for this distribution. To fill this gap, we introduce a class of quantile regression models based on the BS distribution, which allows us to describe positive and asymmetric data when a quantile must be predicted using covariates. We use an approach based on a quantile parameterization to generate the model, permitting us to consider a similar framework to generalized linear models, providing wide flexibility. The methodology proposed includes a thorough study of theoretical properties and practical issues, such as maximum likelihood parameter estimation and diagnostic analytics based on local influence and residuals. The performance of the residuals is evaluated by simulations, whereas an illustrative example of income data is conducted using the methodology to show its potential for applications. The numerical results report an adequate performance of the approach to quantile regression, indicating that the BS distribution is a good modeling choice when dealing with data that have both positive support and asymmetry. The economic implications of our investigation are discussed in the final section. Hence, it can be a valuable addition to the tool kit of applied statisticians and econometricians.

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A new BISARMA time series model for forecasting mortality using weather and particulate matter data

Leiva

Saulo

Souza

et al. 2021

Journal of Forecasting

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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.

show abstract

A Methodology for Data-Driven Decision-Making in the Monitoring of Particulate Matter Environmental Contamination in Santiago of Chile

Cited by 11 publications

References 36 publications

Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model

Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model

Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data

A new BISARMA time series model for forecasting mortality using weather and particulate matter data

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