TITLE:
Comparison of REML and MINQUE for Estimated Variance Components and Predicted Random Effects
AUTHORS:
Nan Nan, Johnie N. Jenkins, Jack C. McCarty, Jixiang Wu
KEYWORDS:
Comparison of REML and MINQUE for Estimated Variance Components and Predicted Random Effects
JOURNAL NAME:
Open Journal of Statistics,
Vol.6 No.5,
October
18,
2016
ABSTRACT: Linear mixed model (LMM) approaches have
been widely applied in many areas of research data analysis because they offer
great flexibility for different data structures and linear model systems. In
this study, emphasis is placed on comparing the properties of two LMM
approaches: restricted maximum likelihood (REML) and minimum norm quadratic
unbiased estimation (MINQUE) with and without resampling techniques being
included. Bias, testing power, Type I error, and computing time were compared
between REML and MINQUE approaches with and without Jackknife technique based
on 500 simulated data sets. Results showed that MINQUE and REML methods
performed equally regarding bias, Type I error, and power. Jackknife-based
MINQUE and REML greatly improved power compared to non-Jackknife based linear
mixed model approaches. Results also showed that MINQUE is more time-saving
compared to REML, especially with the use of resampling techniques and large
data set analysis. Results from the actual cotton data analysis were in
agreement with our simulated results. Therefore, Jackknife-based MINQUE
approaches could be recommended to achieve desirable power with reduced time
for a large data analysis and model simulations.