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. ...

The UK National Student Survey (NSS) represents a major resource, never previously used in the economics literature, for understanding how the market signal of quality in higher education works. In this study, we examine the determinants of the NSS overall student satisfaction score across eleven subject areas for 121 UK universities between 2007 and 2010. Using a unique panel data set and esti...

Linear mixed models are popular models for use with clustered and longitudinal data due to their ability to model variation at different levels of clustering. A Monte Carlo study was used to explore the impact of assumption violations on the bias of parameter estimates and the empirical type I error rates. Simulated conditions included in this study are: simulated serial correlation structure, ...

In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable factors optimizing the overall response or outcome. In the cases when a random effects model is applicable and a fixed effects model is assumed instead, an increase in the variance of the coefficient vector should be expected. In this pa...

Records on 65,536 Landrace pigs collected between 1985 and 1999 in herds on the National Swine Registry STAGES program were used to estimate additive genetic (animal), common environmental (litter), and residual variances and covariances for days to 250 lb, backfat, loin eye area, and lean growth rate. Analysis was by the REMLf90 program of I. Misztal using a multiple-trait animal model with fi...

Random effect analysis has been introduced into fMRI research in order to generalize findings from the study group to the whole population. Generalizing findings is obviously harder than detecting activation within the study group since in order to be significant, an activation has to be larger than the inter-subject variability. Indeed, detected regions are smaller when using random effect ana...

Tensor product smooths provide the natural way of representing smooth interaction terms in regression models because they are invariant to the units in which the covariates are measured, hence avoiding the need for arbitrary decisions about relative scaling of variables. They would also be the natural way to represent smooth interactions in mixed regression models, but for the fact that the ten...