Stress Strength Reliability in Multicomponent Model for Rayleigh-Exponential (Log Logistic) Distribution

Abstract

Multivariate repeated measures (MRM) data, in which multiple outcomes are repeatedly measured at two or more occasions, are commonly collected in several disciplines including medicine, ecology, and environmental sciences, where investigators seek to understand changes in multiple correlated outcomes over time or different occasions1-6. Multivariate repeated measures data are particularly useful for studying evolutions in subjects’ responses over time on multiple characteristics7.For example, Fieuws and Verbeke1reported data on a cohort of patients who having undergone kidney transplant, were longitudinally monitored at irregularly spaced intervals over a 10 year period. The repeated collection of multiple biochemical and physiological markers, which constitute multivariate repeated measures data, were used to predict 10-year success of graft. Multivariate repeated measures data are inherently challenging to analyze because they are typically characterized by non-Gaussian distributions, and high-dimensional data8, 9. Classical classification and prediction models developed for data collected in a cross-sectional study are not appropriate to address the complexities observed in multivariate repeated measures data 8, 9 .