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Copula-based regression models with data missing at random

  • Published:2020-11 Issue: Volume:180 Page:104654
  • ISSN:0047-259X
  • Container-title:Journal of Multivariate Analysis
  • language:en
  • Short-container-title:Journal of Multivariate Analysis

Author:

Hamori Shigeyuki,Motegi Kaiji,Zhang Zheng

Funder

JSPS, Japan

Organization for Advanced and Integrated Research (OAIR), Kobe University

Japan Center for Economic Research

OAIR

Publisher

Elsevier BV

Subject

Statistics, Probability and Uncertainty,Numerical Analysis,Statistics and Probability

Reference33 articles.

1. Empirical process methods in econometrics;Andrews;Handb. Econom.,1994

2. Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting;Chan;J. R. Stat. Soc. Ser. B Stat. Methodol.,2016

3. Prediction based on conditional distributions of vine copulas;Chang;Comput. Statist. Data Anal.,2019

4. Semiparametric copula quantile regression for complete or censored data;De Backer;Electron. J. Stat.,2017

5. Some comments on copula-based regression;Dette;J. Amer. Statist. Assoc.,2014

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1. Parametric and semiparametric approaches for copula-based regression estimation;Hacettepe Journal of Mathematics and Statistics;2024-08-27

2. Copula-based regression estimation in the presence of outliers;Communications in Statistics - Simulation and Computation;2024-07-08

3. A review of predictive uncertainty estimation with machine learning;Artificial Intelligence Review;2024-03-18

4. Copula modeling from Abe Sklar to the present day;Journal of Multivariate Analysis;2023-11

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