Modeling COVID-19 cases statistically and evaluating their effect on the economy of countries
COVID-19 infections have plagued the world and led to deaths with a heavy pneumonia
manifestation. The main objective of this investigation is to evaluate the performance of …
manifestation. The main objective of this investigation is to evaluate the performance of …
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 …
Breakpoint analysis for the COVID-19 pandemic and its effect on the stock markets
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 …
arising from COVID-19 in global markets. Breakpoints in the price series of stock indexes are …
A new principal component analysis by particle swarm optimization with an environmental application for data science
JA Ramirez-Figueroa, C Martin-Barreiro… - … Research and Risk …, 2021 - Springer
In this paper, we propose a new method for disjoint principal component analysis based on
an intelligent search. The method consists of a principal component analysis with …
an intelligent search. The method consists of a principal component analysis with …
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 …
Asymmetric autoregressive models: Statistical aspects and a financial application under COVID-19 pandemic
Y Liu, C Mao, V Leiva, S Liu… - Journal of Applied …, 2022 - Taylor & Francis
In the present study, we provide a motivating example with a financial application under
COVID-19 pandemic to investigate autoregressive (AR) modeling and its diagnostics based …
COVID-19 pandemic to investigate autoregressive (AR) modeling and its diagnostics based …
Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications
Beta regression models have become a popular tool for describing and predicting limited-
range continuous data such as rates and proportions. However, these models can be …
range continuous data such as rates and proportions. However, these models can be …
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 …