Copula-based regression estimation and inference
- Authors
- 노호석; ANOUAR EL GHOUCH; TAOUFIK BOUEZMARNI
- Issue Date
- Jun-2013
- Publisher
- AMER STATISTICAL ASSOC
- Citation
- JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, v.108, no. 502, pp 676 - 688
- Pages
- 13
- Journal Title
- JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Volume
- 108
- Number
- 502
- Start Page
- 676
- End Page
- 688
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/11272
- DOI
- 10.1080/01621459.2013.783842
- ISSN
- 0162-1459
1537-274X
- Abstract
- We investigate a new approach to estimating a regression function based on copulas. The main idea behind this approach is to write the regression function in terms of a copula and marginal distributions. Once the copula and the marginal distributions are estimated, we use the plug-in method to construct our new estimator. Because various methods are available in the literature for estimating both a copula and a distribution, this idea provides a rich and flexible family of regression estimators. We provide some asymptotic results related to this copula-based regression modeling when the copula is estimated via profile likelihood and the marginals are estimated nonparametrically. We also study the finite sample performance of the estimator and illustrate its usefulness by analyzing data from air pollution studies.
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