Copula (linguistics)
View article: A fresh look at Bivariate Binomial Distributions
A fresh look at Bivariate Binomial Distributions Open
Binomial distributions capture the probabilities of `heads' outcomes when a (biased) coin is tossed multiple times. The coin may be identified with a distribution on the two-element set {0,1}, where the 1 outcome corresponds to `head'. One…
View article: A fresh look at Bivariate Binomial Distributions
A fresh look at Bivariate Binomial Distributions Open
Binomial distributions capture the probabilities of `heads' outcomes when a (biased) coin is tossed multiple times. The coin may be identified with a distribution on the two-element set {0,1}, where the 1 outcome corresponds to `head'. One…
View article: Code to: Increasing flexibility for the meta-analysis of full ROC curves - a copula approach
Code to: Increasing flexibility for the meta-analysis of full ROC curves - a copula approach Open
This archive contains code and simulation results for Increasing flexibility for the meta-analysis of full ROC curves - a copula approach. See ReadMe.md for more details.
View article: Present Participle as Predicative with <i>vara</i> ‘be’ in Present Day Swedish
Present Participle as Predicative with <i>vara</i> ‘be’ in Present Day Swedish Open
This article investigates the case of lexical restrictions on present participles with predicate vara ‘be’ in Swedish. According to the standard descriptions, Present Day Swedish disallows the use of verbal present participles in the compl…
View article: A 2D-CFAR Target Detection Method in Sea Clutter Based on Copula Theory Using Dual-Observation Channels
A 2D-CFAR Target Detection Method in Sea Clutter Based on Copula Theory Using Dual-Observation Channels Open
The target detection method based on a constant false alarm rate (CFAR) and feature space is commonly used in remote sensing for detecting maritime targets within sea clutter. However, the performance of traditional CFAR techniques heavily…
View article: Assessing Subway Ridership Resilience under Extreme Weather with Vine Copula Modeling
Assessing Subway Ridership Resilience under Extreme Weather with Vine Copula Modeling Open
Extreme weather poses increasing challenges to urban transit systems, yet the resilience of subway ridership under such conditions remains insufficiently understood. This study develops an hour-specific vine copula framework for New York C…
View article: Research on Corn Income Insurance Pricing under the “Insurance + Futures” Model
Research on Corn Income Insurance Pricing under the “Insurance + Futures” Model Open
Global climate change has increased systemic risks for grain-producing households. It has led to fluctuations in corn yields and prices, which threaten the stability of farmers’ incomes. China has developed an innovative agricultural…
View article: U.S. Economic Policy Uncertainty, Oil Volatility, and Climate Transition After Trump’s Withdrawal from the Paris Agreement
U.S. Economic Policy Uncertainty, Oil Volatility, and Climate Transition After Trump’s Withdrawal from the Paris Agreement Open
Economic Policy Uncertainty (EPU) captures the unpredictability of government decisions that shape market expectations and investment dynamics. In the energy sector, elevated policy uncertainty amplifies volatility, especially in oil marke…
View article: Dataset_Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment
Dataset_Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment Open
Title: Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment 1. Raw data (including wind speed and air density) from six typical sites 2. DataExtraction_QC.py 3.…
View article: Dataset_Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment
Dataset_Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment Open
Title: Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment 1. Raw data (including wind speed and air density) from six typical sites 2. DataExtraction_QC.py 3.…
View article: Data Privatization in Vertical Federated Learning with Client-wise Missing Problem
Data Privatization in Vertical Federated Learning with Client-wise Missing Problem Open
Vertical Federated Learning (VFL) often suffers from client-wise missingness, where entire feature blocks from some clients are unobserved, and conventional approaches are vulnerable to privacy leakage. We propose a Gaussian copulabased fr…
View article: Rank-Based Copula-Adjusted Mann–Kendall (R-CaMK)—A Copula–Vine Framework for Trend Detection and Sensor Selection in Spatially Dependent Environmental Networks
Rank-Based Copula-Adjusted Mann–Kendall (R-CaMK)—A Copula–Vine Framework for Trend Detection and Sensor Selection in Spatially Dependent Environmental Networks Open
A Rank-Based Copula-Adjusted Mann–Kendall (R-CaMK) is proposed, with an end-to-end mathematical and computational framework that integrates rank-based multivariate dependence modelling (regular vines where data permit, Gaussian copula fall…
View article: COMPARISON OF COPULA FAMILY (GAUSSIAN, ARCHIMEDEAN, AND REGRESSION) IN A CASE STUDY OF COMPOSITE STOCK PRICE INDEX ON INDONESIA STOCK EXCHANGE
COMPARISON OF COPULA FAMILY (GAUSSIAN, ARCHIMEDEAN, AND REGRESSION) IN A CASE STUDY OF COMPOSITE STOCK PRICE INDEX ON INDONESIA STOCK EXCHANGE Open
Stocks are one of the most popular financial market instruments. On the other hand, stocks are an investment instrument that is widely chosen by investors because stocks are able to provide attractive profit levels. Investment is an effort…
View article: Physics-Informed Multi-Task Neural Network (PINN) Learning for Ultra-High-Performance Concrete (UHPC) Strength Prediction
Physics-Informed Multi-Task Neural Network (PINN) Learning for Ultra-High-Performance Concrete (UHPC) Strength Prediction Open
Ultra-high-performance concrete (UHPC) mixtures exhibit tightly coupled ingredient–property relations and heterogeneous reporting, which complicate the data-driven prediction of compressive and flexural strength. We present an end-to-end f…
View article: Distribution-Free Inference Beyond Exchangeability
Distribution-Free Inference Beyond Exchangeability Open
The paradigm of exchangeability has long served as a cornerstone for robust statistical inference, particularly within non-parametric and distribution-free methods. It simplifies complex dependency structures into an assumption that observ…
View article: Distribution-Free Inference Beyond Exchangeability
Distribution-Free Inference Beyond Exchangeability Open
The paradigm of exchangeability has long served as a cornerstone for robust statistical inference, particularly within non-parametric and distribution-free methods. It simplifies complex dependency structures into an assumption that observ…
View article: Distribution-Free Inference Beyond Exchangeability
Distribution-Free Inference Beyond Exchangeability Open
The paradigm of exchangeability has long served as a cornerstone for robust statistical inference, particularly within non-parametric and distribution-free methods. It simplifies complex dependency structures into an assumption that observ…
View article: A Review: Construction of Statistical Distributions
A Review: Construction of Statistical Distributions Open
Statistical modeling is fundamentally based on probability distributions, which can be discrete or continuous and univariate or multivariate. This review focuses on the methods used to construct these distributions, covering both tradition…
View article: Extending the Accelerated Failure Conditionals Model to Location-Scale Families
Extending the Accelerated Failure Conditionals Model to Location-Scale Families Open
Arnold and Arvanitis (2020) introduced a novel class of bivariate conditionally specified distributions, in which dependence between two random variables is established by defining the distribution of one variable conditional on the other.…
View article: Copula Dependent Censoring Models for Survival Prognosis: Application to Lactylation-Related Genes
Copula Dependent Censoring Models for Survival Prognosis: Application to Lactylation-Related Genes Open
Survival for cancer patients is predictable by gene expressions obtained from DNA microarrays for tumor samples. For analyzing survival data with gene expressions, traditional survival analysis methods have been employed. However, these me…
View article: Parametric inference for the Mann–Whitney effect under survival copula models
Parametric inference for the Mann–Whitney effect under survival copula models Open
The Mann–Whitney effect is one of the most important measures for comparing the survival times of two independent groups. Under the independence assumption of two survival times, the Mann-Whitney effect can be estimated by Efron’s classica…
View article: Estimation problems for some perturbations of the independence copula
Estimation problems for some perturbations of the independence copula Open
This work provides a study of copula parameters estimators based on functions of Markov chains they generate. Copulas of interest are perturbations of the independence copula. We provide asymptotic distributions of maximum likelihood estim…
View article: Extending the Accelerated Failure Conditionals Model to Location-Scale Families
Extending the Accelerated Failure Conditionals Model to Location-Scale Families Open
Arnold and Arvanitis (2020) introduced a novel class of bivariate conditionally specified distributions, in which dependence between two random variables is established by defining the distribution of one variable conditional on the other.…
View article: Symmetric Bernoulli distributions and minimal dependence copulas
Symmetric Bernoulli distributions and minimal dependence copulas Open
View article: Generalized FGM dependence: geometrical representation and convex bounds on sums
Generalized FGM dependence: geometrical representation and convex bounds on sums Open
Building on the one-to-one relationship between generalized FGM copulas and multivariate Bernoulli distributions, we prove that the class of multivariate distributions with generalized FGM copulas is a convex polytope. Therefore, we find s…
View article: CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data
CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data Open
High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challengin…
View article: Models with Accelerated Failure Conditionals
Models with Accelerated Failure Conditionals Open
Arnold and Arvanitis (2020) introduced a novel bivariate conditionally specified distribution, a distribution in which dependence between two random variables is established by defining the distribution of one variable conditional on the o…
View article: Models with Accelerated Failure Conditionals
Models with Accelerated Failure Conditionals Open
Arnold and Arvanitis (2020) introduced a novel bivariate conditionally specified distribution, a distribution in which dependence between two random variables is established by defining the distribution of one variable conditional on the o…
View article: Copula‐Based Deep Learning Models for Competing Risks
Copula‐Based Deep Learning Models for Competing Risks Open
This study introduces a novel approach to modeling competing risks in survival analysis by integrating learnable Copula functions (Clayton, Frank, and Gaussian) with deep learning architectures, including Convolutional Neural Networks (CNN…
View article: A Copula-Based Model for Analyzing Bivariate Offense Data
A Copula-Based Model for Analyzing Bivariate Offense Data Open
We developed a class of bivariate integer-valued time series models using copula theory. Each count time series is modeled as a Markov chain, with serial dependence characterized through copula-based transition probabilities for Poisson an…