Rank-Based methods for iid linear models have been developed over the past 30 years. However, little work has been done in the area of mixed models. In this paper, we discuss a transformation approach to modeling a particular mixed model: one with an arbitrary number of fixed effects and covariates but only one random effect. Discussion of the asymptotic theory is given and the results of a sim...

This paper considers the problem of estimating fixed effects, random effects and variance components for the multi-variate random effects model with complete and incomplete data. It also considers making inference about the fixed and random effects, a problem which requires careful consideration of the choice of degrees of freedom to use in confidence intervals. This paper uses the EM algorithm...

Several organizations have developed online crowdsourcing platforms that tap into the “wisdom” (creative ideas for new products and services) of a large “crowd’ of non-experts (consumers). The crowdsourcing model seems very attractive because consumers are intrinsically motivated to freely contribute their creative ideas, but little is known about the effectiveness of these approaches. Two year...

Robust rank-based methods are proposed for the analysis of data from multi-center clinical trials using a mixed model (including covariates) in which the treatment effects are assumed to be fixed and the center effects are assumed to be random. These rankbased methods are developed under the usual mixed model structure but without the normality assumption of the random components in the model. ...

This paper uses player/match level data drawn from five playing seasons of the English Premiership League (EPL) to test for the presence of a refereeing susceptibility to social pressure in the application of soccer’s commonest sanction, the yellow disciplinary card. Using both player-specific fixed and random effects models, tentative support for the proposition is uncovered. The estimated eff...

Repeated measures refer to the observations taken on the same subject (plant, animal, person, etc). There is usually a variation among the subjects, which is modeled by random effects. Here is an example. A completely randomized design with a single factor (with fertilizer or without). Each treatment is applied to three plants and the root length of each plant is measured on five occasions (wee...

Estimation of parameters and random effects using true maximum likelihood methods is compared to the commonly used penalized maximum likelihood method. The simulated catch-at-age datasets have all conceivable noise in stock and fishing dynamics in addition to the observation error on the catch. Improvement is modest in simple models but refinements that are only possible with these methods prov...

For inferences from random-effect models Lee and Nelder (1996) proposed to use hierarchical likelihood (h-likelihood). It allows inference from models that may include both fixed and random parameters. Because of the presence of unobserved random variables h-likelihood is not a likelihood in the Fisherian sense. The Fisher likelihood framework has advantages such as generality of application, s...

In this paper we consider the relation between the volume deceleration parameter obtained within the Buchert averaging scheme and the deceleration parameter derived from supernova observation. This work was motivated by recent findings that showed that there are models which despite having Λ = 0 have volume deceleration parameter qvol < 0. This opens the possibility that back-reaction and avera...