Birnbaum‐Saunders quantile regression and its diagnostics with application to economic data
Abstract 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 …
statistical literature and has proved to be very versatile and efficient across a wide range of …
Birnbaum-Saunders quantile regression models with application to spatial data
In the present paper, a novel spatial quantile regression model based on the Birnbaum–
Saunders distribution is formulated. This distribution has been widely studied and applied in …
Saunders distribution is formulated. This distribution has been widely studied and applied in …
Cokriging prediction using as secondary variable a functional random field with application in environmental pollution
Cokriging is a geostatistical technique that is used for spatial prediction when realizations of
a random field are available. If a secondary variable is cross-correlated with the primary …
a random field are available. If a secondary variable is cross-correlated with the primary …
A new BISARMA time series model for forecasting mortality using weather and particulate matter data
Abstract 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 …
statistical literature and has proved to be very versatile and efficient across a wide range of …
Atmospheric PM2. 5 prediction using DeepAR optimized by sparrow search algorithm with opposition-based and fitness-based learning
F Jiang, X Han, W Zhang, G Chen - Atmosphere, 2021 - mdpi.com
There is an important significance for human health in predicting atmospheric concentration
precisely. However, due to the complexity and influence of contingency, atmospheric …
precisely. However, due to the complexity and influence of contingency, atmospheric …
Global and local diagnostic analytics for a geostatistical model based on a new approach to quantile regression
Data with spatial dependence are often modeled by geoestatistical tools. In spatial
regression, the mean response is described using explanatory variables with georeferenced …
regression, the mean response is described using explanatory variables with georeferenced …
Modeling environmental pollution using varying-coefficients quantile regression models under log-symmetric distributions
L Sánchez, G Ibacache-Pulgar, C Marchant… - Axioms, 2023 - mdpi.com
Many phenomena can be described by random variables that follow asymmetrical
distributions. In the context of regression, when the response variable Y follows such a …
distributions. In the context of regression, when the response variable Y follows such a …
Robust three-step regression based on comedian and its performance in cell-wise and case-wise outliers
Both cell-wise and case-wise outliers may appear in a real data set at the same time. Few
methods have been developed in order to deal with both types of outliers when formulating …
methods have been developed in order to deal with both types of outliers when formulating …
Determinant powers of socioeconomic factors and their interactive impacts on particulate matter pollution in North China
Severe air pollution has significantly impacted climate and human health worldwide. In this
study, global and local Moran's I was used to examine the spatial autocorrelation of PM2. 5 …
study, global and local Moran's I was used to examine the spatial autocorrelation of PM2. 5 …
Machine Learning Models to Predict Critical Episodes of Environmental Pollution for PM2. 5 and PM10 in Talca, Chile
G Carreño, XA López-Cortés, C Marchant - Mathematics, 2022 - mdpi.com
One of the main environmental problems that affects people's health and quality of life is air
pollution by particulate matter. Chile has nine of the ten most polluted cities in South …
pollution by particulate matter. Chile has nine of the ten most polluted cities in South …