An errors-in-variables model based on the Birnbaum–Saunders distribution and its diagnostics with an application to earthquake data

Carrasco, Jalmar M. F.; Figueroa-Zúñiga, Jorge; Leiva, Víctor; Riquelme, Marco; Aykroyd, Robert G.

doi:10.1007/s00477-020-01767-3

Cited by 23 publications

(17 citation statements)

References 44 publications

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“…An important aspect to be further studied is to measure the efficiency and impact of the relevant variables and factors on the stock markets [ 53 , 54 ]. Extensions to the multivariate case [ 55 ] and the incorporation of temporal [ 56 ], spatial [ 52 , 57 ], and quantile regression [ 57 ] structures in the modeling, as well as errors-in-variables [ 58 ], and PLS regression [ 44 ], are also of practical relevance.…”

Section: Discussion Conclusion Limitations and Future Researcmentioning

confidence: 99%

Breakpoint Analysis for the COVID-19 Pandemic and Its Effect on the Stock Markets

Chahuán-Jiménez

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Fuente‐Mella

et al. 2021

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In this research, statistical models are formulated to study the effect of the health crisis arising from COVID-19 in global markets. Breakpoints in the price series of stock indexes are considered. Such indexes are used as an approximation of the stock markets in different countries, taking into account that they are indicative of these markets because of their composition. The main results obtained in this investigation highlight that countries with better institutional and economic conditions are less affected by the pandemic. In addition, the effect of the health index in the models is associated with their non-significant parameters. This is due to that the health index used in the modeling would not determine the different capacities of the countries analyzed to respond efficiently to the pandemic effect. Therefore, the contagion is the preponderant factor when analyzing the structural breakdown that occurred in the world economy.

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Section: Discussion Conclusion Limitations and Future Researcmentioning

confidence: 99%

Breakpoint Analysis for the COVID-19 Pandemic and Its Effect on the Stock Markets

Chahuán-Jiménez

Rubilar

Fuente‐Mella

et al. 2021

Entropy

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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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“…(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]. (vi) Robust estimation methods when outliers are present into the data set can be used [63].…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

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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An errors-in-variables model based on the Birnbaum–Saunders distribution and its diagnostics with an application to earthquake data

Abstract: An errors-invariables model based on the Birnbaum-Saunders and its diagnostics with an application to earthquake data. Stochastic Environmental Research and Risk Assessment, 34 (2). pp. 369-380.

Cited by 23 publications

References 44 publications

Breakpoint Analysis for the COVID-19 Pandemic and Its Effect on the Stock Markets

Breakpoint Analysis for the COVID-19 Pandemic and Its Effect on the Stock Markets

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

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