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Improved confidence interval for average annual percent change in trend analysis
Author(s) -
Kim HyuneJu,
Luo Jun,
Chen HuannSheng,
Green Don,
Buckman Dennis,
Byrne Jeffrey,
Feuer Eric J.
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7344
Subject(s) - confidence interval , statistics , resampling , mathematics , regression analysis , regression , interval (graph theory) , sample (material) , robust confidence intervals , sample size determination , cdf based nonparametric confidence interval , distribution (mathematics) , econometrics , computer science , mathematical analysis , chemistry , chromatography , combinatorics
This paper considers an improved confidence interval for the average annual percent change in trend analysis, which is based on a weighted average of the regression slopes in the segmented line regression model with unknown change points. The performance of the improved confidence interval proposed by Muggeo is examined for various distribution settings, and two new methods are proposed for further improvement. The first method is practically equivalent to the one proposed by Muggeo, but its construction is simpler, and it is modified to use the t ‐distribution instead of the standard normal distribution. The second method is based on the empirical distribution of the residuals and the resampling using a uniform random sample, and its satisfactory performance is indicated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.