In our study, we have extended the Neyman smooth-type goodness of fit tests by Eubank (1997) from simple random sample to complex surveys (Methodologies have been provided for complex surveys, and theorems have been provided only for stratified random samples.) by incorporating consistent estimators under the survey design, which is accomplished by a data-driven nonparametric order selection method. Simulation results show that these proposed methods potentially improve the statistical power while controlling the type I error very well compared to those commonly used existing test procedures, especially for the cases with slow-varying probabilities. We also derived the large sample properties of the test statistics in stratified sampling.
Level of Degree
Mathematics & Statistics
First Committee Member (Chair)
Second Committee Member
Third Committee Member
Goodness of fit, Neyman smooth, Order selection, The first order and second order corrected tests, Stratification, Clustering, Simple Random Sample
Zhou, Lang. "Neyman Smooth-Type Goodness of Fit Tests in Complex Surveys." (2016). https://digitalrepository.unm.edu/math_etds/57