One-way classification with random effects: A reversed-hazard-based approach

This paper deals with the one-way classification analysis on the basis of conditional proportional reversed hazard rate models when treatment effects are random. Characteristics of the response variable including reversed hazard function, mean, variance and intraclass correlation are derived in explicit expressions. Estimations of the proposed model parameters are also obtained. Best predictions for score effects are given. The proposed model is an alternative approach for one-way classifications in which generalized linear models are applied for analysing continuous responses. For illustrative purposes, a real data set is analysed using the obtained results. (C) 2018 Elsevier B.V. All rights reserved.