Global and local diagnostic analytics for a geostatistical model based on a new approach to quantile regression

Leiva, Víctor; Sánchez, Luis; Galea, Manuel; Saulo, Helton

doi:10.1007/s00477-020-01831-y

Cited by 20 publications

(15 citation statements)

References 55 publications

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“…(iv) Incorporation of temporal, spatial, functional, and quantile regression structures in the modeling, as well as errors-in-variables, and PLS regression, are also of interest [26,29,30,[58][59][60][61][62][63].…”

Section: Conclusion Discussion and Future Researchmentioning

confidence: 99%

“…The second one corresponds to local influence diagnostics that allows us to identify cases that, under small perturbations in the model or in the data, may cause disproportionate changes in the estimates of the model parameters; see details in, for example, Refs. [22,[24][25][26][27][28]30,[37][38][39].…”

Section: Data-influence Analytics In Mixed-effects Logistic Regressiomentioning

confidence: 99%

“…There are several manners to make this validation in models for binary data [19]. Recent advances in model checking and diagnostics have been developed by several authors [20][21][22][23][24][25][26][27][28][29][30]. For more details and references regarding to statistical diagnostics, see Section 3.…”

Section: Introduction and Context Of The Empirical Applicationmentioning

confidence: 99%

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Data-Influence Analytics in Predictive Models Applied to Asthma Disease

et al. 2020

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Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In this work, a methodology of data-influence analytics based on mixed-effects logistic regression models is proposed for detecting potentially influential observations which can affect the quality of these models. Global and local influence diagnostic techniques are used simultaneously in this detection, which are often used separately. In addition, predictive performance measures are considered for this analytics. A study with children and adolescent asthma real data, collected from a public hospital of São Paulo, Brazil, is conducted to illustrate the proposed methodology. The results show that the influence diagnostic methodology is helpful for obtaining an accurate predictive model that provides scientific evidence when data-driven medical decision-making.

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Section: Conclusion Discussion and Future Researchmentioning

confidence: 99%

Section: Data-influence Analytics In Mixed-effects Logistic Regressiomentioning

confidence: 99%

Section: Introduction and Context Of The Empirical Applicationmentioning

confidence: 99%

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Data-Influence Analytics in Predictive Models Applied to Asthma Disease

et al. 2020

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“…(iii) An extension of the present study to the multivariate case is also of practical relevance [50][51][52]. (iv) Incorporation of temporal, spatial, functional, and quantile regression structures in the modeling, as well as errors-in-variables, and PLS regression, are also of interest [53][54][55][56][57][58][59][60][61] . (v) The derivation of diagnostic techniques to detect potential influential cases are needed, which are an important tool to be used in all statistical modeling [7,58,62].…”

Section: Conclusion and Future Researchmentioning

confidence: 92%

A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess

et al. 2020

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In this paper, we consider skew-normal distributions for constructing new a distribution which allows us to model proportions and rates with zero/one inflation as an alternative to the inflated beta distributions. The new distribution is a mixture between a Bernoulli distribution for explaining the zero/one excess and a censored skew-normal distribution for the continuous variable. The maximum likelihood method is used for parameter estimation. Observed and expected Fisher information matrices are derived to conduct likelihood-based inference in this new type skew-normal distribution. Given the flexibility of the new distributions, we are able to show, in real data scenarios, the good performance of our proposal.

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“…The use of the BS distribution has been justified by the proportionate-effect model demonstrating that this distribution has properties similar to those corresponding to the log-normal distribution, which allows its employ in atmospheric pollutant models [34]. For other applications of the BS distribution to environmental phenomena, see [35][36][37][38].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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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Global and local diagnostic analytics for a geostatistical model based on a new approach to quantile regression

Cited by 20 publications

References 55 publications

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

Data-Influence Analytics in Predictive Models Applied to Asthma Disease

A Family of Skew-Normal Distributions for Modeling Proportions and Rates with Zeros/Ones Excess

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

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