Development in Copula Applications in Forestry and Environmental Sciences

Bhatti, M. Ishaq; Quang, Hung

doi:10.1007/978-981-15-1476-0_13

Cited by 5 publications

(3 citation statements)

References 82 publications

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“…We resolve this potential limitation using the vine copulas. Vine copulas (Bhatti and Do 2020;Kurowicka and Cooke 2007) are a flexible graphical model for addressing multivariate copulas based on bivariate copulas, so-called pair-copulas. Decomposing multivariate probability density into bivariate copulas by independently choosing pair-copulas from the other, a vine copula allows vast flexibility in modeling dependence.…”

Section: Methodsmentioning

confidence: 99%

The Changing Dynamics of Board Independence: A Copula Based Quantile Regression Approach

Kim

Cho

Jun

et al. 2020

JRFM

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This paper examines the effect of board characteristics, especially board independence, on firm performance from a dynamic perspective through copula-based quantile regression approaches, which allow us to focus on changes at different points in the distribution of board characteristics. We find that the effect of board independence on Tobin’s Q, a proxy of firm value, is negatively associated with firm value, using ordinary least squares (OLS) regression. This negative effect using the conditional mean of the firm value does not hold across the conditional quantiles of the distribution of Tobin’s Q, and this finding is still held under both the linear and the nonlinear quantile regressions. We even lessen the assumption of distributions of multivariate board variables by employing parametric copula-based quantile regressions as well as nonparametric ones. The results support our findings. Our results suggest that estimating the quantile effect of board variables on firm value can provide more meaningful insight than just examining the conditional mean effect.

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Section: Methodsmentioning

confidence: 99%

The Changing Dynamics of Board Independence: A Copula Based Quantile Regression Approach

Kim

Cho

Jun

et al. 2020

JRFM

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“…The normal copula function is defined using a multi-dimensional standard normal probability density function and a marginal probability density function to obtain the joint distribution. It can be used to model multi-dimensional joint distributions and is suitable for forest growth analysis studies [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Statistical Dependence Framework Based on a Multivariate Normal Copula Function and Stochastic Differential Equations for Multivariate Data in Forestry

Krikštolaitis

Mozgeris

Petrauskas

et al. 2023

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Stochastic differential equations and Copula theories are important topics that have many advantages for applications in almost every discipline. Many studies in forestry collect longitudinal, multi-dimensional, and discrete data for which the amount of measurement of individual variables does not match. For example, during sampling experiments, the diameters of all trees, the heights of approximately 10% of the trees, and the tree crown base height and crown width for a significantly smaller number of trees are measured. In this study, for estimating five-dimensional dependencies, we used a normal copula approach, where the dynamics of individual tree variables (diameter, potentially available area, height, crown base height, and crown width) are described by a stochastic differential equation with mixed-effect parameters. The approximate maximum likelihood method was used to obtain parameter estimates of the presented stochastic differential equations, and the normal copula dependence parameters were estimated using the pseudo-maximum likelihood method. This study introduced the normalized multi-dimensional interaction information index based on differential entropy to capture dependencies between state variables. Using conditional copula-type probability density functions, the exact form equations defining the links among the diameter, potentially available area, height, crown base height, and crown width were derived. All results were implemented in the symbolic algebra system MAPLE.

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“…Presently, copula models have become a favored statistical tool to describe the association between variables. These models have been applied in different areas, including the study of infectious diseases (e.g., see [6]) and environmental science (e.g., see [7]). One important benefit of using a copula model is that one can model the marginal distribution independently from the dependency structures, which are completely captured via the copula function.…”

Section: Introductionmentioning

confidence: 99%

The spread of COVID-19 at Hot-Temperature Places With Different Curfew Situations Using Copula Models

Alanazi

2021

2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)

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The infectious coronavirus disease 2019 (COVID-19) has become a serious global pandemic. Different studies have shown that increasing temperature can play a crucial role in the spread of the virus. Most of these studies were limited to winter or moderate temperature levels and were conducted using conventional models. However, traditional models are too simplistic to investigate complex, non-linear relationships and suffer from some restrictions. Therefore, we employed copula models to examine the impact of high temperatures on virus transmission. The findings from the copula models showed that there was a weak to moderate effect of temperature on the number of infections and the effect almost vanished under a lockdown policy. Therefore, this study provides new insight into the relationship between COVID-19 and temperature, both with and without social isolation practices. Such results can lead to improvements in our understanding of this new virus. In particular, the results derived from the copula models examined here, unlike existing traditional models, provide evidence that there is no substantial influence of high temperatures on the active COVID-19 outbreak situation. In addition, the results indicate that the transmission of COVID-19 is strongly influenced by social isolation practices.

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Development in Copula Applications in Forestry and Environmental Sciences

Cited by 5 publications

References 82 publications

The Changing Dynamics of Board Independence: A Copula Based Quantile Regression Approach

The Changing Dynamics of Board Independence: A Copula Based Quantile Regression Approach

A Statistical Dependence Framework Based on a Multivariate Normal Copula Function and Stochastic Differential Equations for Multivariate Data in Forestry

The spread of COVID-19 at Hot-Temperature Places With Different Curfew Situations Using Copula Models

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