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Goodness‐of‐fit tests for distributions estimated from complex survey data
Author(s) -
Lohr Sharon L.,
Riddles Minsun K.,
Brick J. Michael
Publication year - 2019
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11501
Subject(s) - nonparametric statistics , statistics , goodness of fit , national health and nutrition examination survey , empirical distribution function , mathematics , parametric statistics , survey data collection , statistical hypothesis testing , econometrics , distribution (mathematics) , index (typography) , demography , computer science , sociology , mathematical analysis , population , world wide web
We propose nonparametric procedures for comparing the empirical distribution function of data from a complex survey with a hypothesized parametric reference distribution. The hypothesized distribution may be fully specified, or it may be a family with the parameters to be estimated from the data. Of the procedures studied, a modification of the Cramér–von Mises test proposed by Lockhart, Spinelli & Stephens [Lockhart, Spinelli and Stephens, The Canadian Journal of Statistics 2007; 35, 125–133] is supported theoretically and performs well in two simulation studies. The methods are applied to examine the distribution of body mass index in the U.S. National Health and Nutrition Examination Survey. The Canadian Journal of Statistics 47: 409–425; 2019 © 2019 Statistical Society of Canada