English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In many practical scenarios (e.g., finance, system reliability, etc.), it is often of interest to estimate a bivariate distribution and test for some desired association properties like positive quadrant dependent (PQD) or negative quadrant dependent (NQD). Often estimation and testing for PQD/NQD property are performed using copula models as it then eliminates the need for estimating marginal distributions. Many parametric copula families have been used that allow for controlling the PQD/NQD property by a finite dimensional parameter (often just real-valued) and the problem reduces to the straightforward estimation and testing for fixed dimensional parameter using standard statistical methodologies (e.g., maximum likelihood). This article extends such a line of work by dropping any parametric assumptions and provides a fully data-dependent automated approach to estimate a copula and test for PQD property. The estimator is shown to be large-sample consistent under a set of mild regularity conditions. Numerical illustrations based on simulated data are also provided to compare the performance of the proposed testing procedure with some available methods and applications to real case studies are also provided.

Nonparametric Estimation and Testing for Positive Quadrant Dependent Bivariate Copula