Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Zhou, Qian
Committee Member
Patil, Prakash N.
Committee Member
Wu, Tung-Lung
Date of Degree
8-7-2020
Document Type
Graduate Thesis - Open Access
Major
Statistics
Degree Name
Master of Science
College
College of Arts and Sciences
Department
Department of Mathematics and Statistics
Abstract
In this thesis, we suggest a goodness-ofit test for semi-parametric copula models. We extended the pseudo in-and-out-sample (PIOS) test proposed in [17], which is based on the PIOS test in [28]. The PIOS test is constructed by comparing the pseudo "in-sample" likelihood and pseudo "out-of-sample" likelihood. Our contribution is twoold. First, we use the approximate test statistics instead of the exact test statistics to alleviate the computational burden of calculating the test statistics. Secondly, we propose a parametric bootstrap procedure to approximate the distribution of the test statistic. Unlike the nonparametric bootstrap which resamples from the original data, the parametric procedure resamples the data from the copula model under the null hypothesis. We conduct simulation studies to investigate the performance of the approximate test statistic and parametric bootstrap. The results show that the parametric bootstrap presents higher test power with a well-controlled type I error compared to the nonparametric bootstrap.
URI
https://hdl.handle.net/11668/18450
Recommended Citation
Shin, Jimin, "A goodness-ofit test for semi-parametric copula models for bivariate censored data" (2020). Theses and Dissertations. 152.
https://scholarsjunction.msstate.edu/td/152