Simultaneous Confidence Intervals for Pairwise Multiple Comparisons in a Two-Way Unbalanced Design

Biometrics ◽  
1996 ◽  
Vol 52 (2) ◽  
pp. 463 ◽  
Author(s):  
Siu Hung Cheung ◽  
Wai Sum Chan
Keyword(s):  
Confidence Intervals ◽  
Multiple Comparisons ◽  
Simultaneous Confidence Intervals ◽  
Unbalanced Design

scholarly journals Multiple comparisons of precipitation variations in different areas using simultaneous confidence intervals for all possible ratios of variances of several zero-inflated lognormal models

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12659
Author(s):  
Patcharee Maneerat ◽  
Sa-Aat Niwitpong
Keyword(s):  
Confidence Intervals ◽  
Large Population ◽  
Parametric Bootstrap ◽  
Multiple Comparisons ◽  
Precipitation Data ◽  
Injury Death ◽  
Simultaneous Confidence Intervals ◽  
Southern Thailand ◽  
Precipitation Variation ◽  
Weather Stations

Flash flooding and landslides regularly cause injury, death, and homelessness in Thailand. An advancedwarning system is necessary for predicting natural disasters, and analyzing the variability of daily precipitation might be usable in this regard. Moreover, analyzing the differences in precipitation data among multiple weather stations could be used to predict variations in meteorological conditions throughout the country. Since precipitation data in Thailand follow a zero-inflated lognormal (ZILN) distribution, multiple comparisons of precipitation variation in different areas can be addressed by using simultaneous confidence intervals (SCIs) for all possible pairwise ratios of variances of several ZILN models. Herein, we formulate SCIs using Bayesian, generalized pivotal quantity (GPQ), and parametric bootstrap (PB) approaches. The results of a simulation study provide insight into the performances of the SCIs. Those based on PB and the Bayesian approach via probability matching with the beta prior performed well in situations with a large amount of zero-inflated data with a large variance. Besides, the Bayesian based on the reference-beta prior and GPQ SCIs can be considered as alternative approaches for small-to-large and medium-to-large sample sizes from large population, respectively. These approaches were applied to estimate the precipitation variability among weather stations in lower southern Thailand to illustrate their efficacies.

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