Assessing Data Support for the Simplifying Assumption in Bivariate Conditional Copulas

09/27/2019
by   Evgeny Levi, et al.
0

The paper considers the problem of establishing data support for the simplifying assumption (SA) in a bivariate conditional copula model. It is known that SA greatly simplifies the inference for a conditional copula model, but standard tools and methods for testing SA tend to not provide reliable results. After splitting the observed data into training and test sets, the method proposed will use a flexible training data Bayesian fit to define tests based on randomization and standard asymptotic theory. Theoretical justification for the method is provided and its performance is studied using simulated data. The paper also discusses implementations in alternative models of interest, e.g. Gaussian, Logistic and Quantile regressions.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

07/23/2018

Prediction based on conditional distributions of vine copulas

Vine copula models are a flexible tool in multivariate non-Gaussian dist...
09/22/2012

An efficient model-free estimation of multiclass conditional probability

Conventional multiclass conditional probability estimation methods, such...
11/22/2021

Implicit Quantile Neural Networks for Jet Simulation and Correction

Reliable modeling of conditional densities is important for quantitative...
05/12/2020

A theoretical treatment of conditional independence testing under Model-X

For testing conditional independence (CI) of a response Y and a predicto...
06/27/2012

A Permutation Approach to Testing Interactions in Many Dimensions

To date, testing interactions in high dimensions has been a challenging ...
09/28/2020

Calibration methods for spatial Data

In an environmental framework, extreme values of certain spatio-temporal...
04/23/2022

Local Gaussian process extrapolation for BART models with applications to causal inference

Bayesian additive regression trees (BART) is a semi-parametric regressio...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.