Results 1 to 10 of about 361 (49)
Scientific Reports, 2022 Copula functions are widely used for modeling multivariate dependence. Since the multivariate data may not necessarily be linear and Gaussian, the copula model is very often brought into the picture for modeling such multivariate phenomena.
K. Dinda, B. Samanta, D. Chakravarty
doaj +1 more source
VALUE AT RISK ANALYSIS ON BLUE CHIP STOCKS PORTFOLIO WITH GAUSSIAN COPULA
Barekeng, 2023 Value at Risk (VaR) is a risk measurement tool to calculate the estimated maximum investment loss with a certain confidence level and period. VaR calculations using financial data are often not normally distributed, so the copula method is used, which is
Tiffany Ardhitha +2 more
doaj +1 more source
Scientific Reports, 2022 In environmental monitoring, multiple spatial variables are often sampled at a geographical location that can depend on each other in complex ways, such as non-linear and non-Gaussian spatial dependence.
Mohomed Abraj +2 more
doaj +1 more source
A New Form of Farlie-Gumbel-Morgenstern Copula: A Comparative Simulation Study and Application
Journal of Statistical Theory and Applications (JSTA), 2021 In this study a new mixture copula has been obtained by creating a linear combination of product copula with copula which is obtained with marginal distributions of order statistics which was proposed by Dolati and Úbeda-Flores, Kybernetika.
Ferhan Baş Kaman, Hülya Olmuş
doaj +1 more source
A Class of Copula-Based Bivariate Poisson Time Series Models with Applications
Computation, 2021 A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson ...
Mohammed Alqawba +2 more
doaj +1 more source
Journal of Hydrology: Regional Studies, 2016 Study region: Narew River in Northeastern Poland. Study focus: Three methods for frequency analysis of snowmelt floods were compared. Two dimensional (2D) normal distribution and copula-based 2D probability distributions were applied to statistically ...
Bogdan Ozga-Zielinski +4 more
doaj +1 more source
Gaussian mixture model for extreme wind turbulence estimation [PDF]
Wind Energy Science, 2022 Uncertainty quantification is necessary in wind turbine design due to the random nature of the environmental inputs, through which the uncertainty of structural loads and response under specific situations can be quantified. Specifically, wind turbulence
X. Zhang, A. Natarajan
doaj +1 more source
A Fully Pipelined Probability Density Function Engine for Gaussian Copula Model
Tsinghua Science and Technology, 2014 The Gaussian Copula Probability Density Function (PDF) plays an important role in the fields of finance, hydrological modeling, biomedical study, and texture retrieval.
Huabin Ruan +3 more
doaj +1 more source
Gaussian Copula Regression in R
Journal of Statistical Software, 2017 This article describes the R package gcmr for fitting Gaussian copula marginal regression models. The Gaussian copula provides a mathematically convenient framework to handle various forms of dependence in regression models arising, for example, in time ...
Guido Masarotto, Cristiano Varin
doaj +1 more source
Correlation between Rainfall and Runoff in Fuchun River Basin Based on Copula Functions and Kernel Density Estimation [PDF]
长江科学院院报[Objective] Rainfall and runoff are two important hydrological variables in river basins, exhibiting the characteristic of random distribution. In-depth analysis of the relationship between rainfall and runoff holds significant importance for watershed ...
YANG Sheng-mei, ZHU De-kang, CHENG Xiang, LI Bo, ZHU Yan-ze, MA Wen-sheng
doaj +1 more source