The t -distribution allows the incorporation of outlier robustness into statistical models while retaining the elegance of likelihood-based inference. In this paper we develop and implement a linear mixed model for the general design of the linear mixed model using the univariate t -distribution. This general design allows a considerably richer class of models to be fit than is possible with ex...

The survey process as measured by paradata may be different for different respondents. Those initially reluctant to participate may be convinced by the interviewer to cooperate, or the reluctance may produce attrition. Difficulty to contact respondents may be related to interviewer effort and busy respondent schedules, or it may be a form of reluctance. This paper will use mixed models to attem...

The learning curves of optimisation algorithms, plotting the evolution of the objective vs. runtime spent. can be viewed as a sample of longitudinal data. In this paper we describe mixed-effects modeling, a standard technique in longitudinal data analysis, and give an example of its application to algorithm performance modeling.

Intelligibility of speech in noise becomes lower as the listeners age increases, even when no apparent hearing impairment is present. The losses are, however, different depending on the nature of the noise and the characteristics of the voice. In this paper we investigate the effect that age, noise type and speaking style have on the intelligibility of speech reproduced by car loudspeakers. Usi...

In classical problems, e.g., comparing two populations, fitting a regression surface, etc., variability is a nuisance parameter. The term “nuisance parameter” is meant here in both the technical and the practical sense. However, there are many instances where understanding the structure of variability is just as central as understanding the mean structure. The purpose of this article is to revi...

Pseudoreplication is the failure of a statistical analysis to properly incorporate the true structure of randomness present in the data. It has been well documented and studied in the ecological literature but has received little attention in the fisheries literature. Avoiding pseudoreplication in analyses of fisheries data can be difficult due to the complexity of the statistical procedures re...

An unknown prior density g(θ) has yielded realizations Θ1,Θ2, . . . ,ΘN . They are unobservable, but each Θi produces an observable value Xi according to a known probability mechanism, for instance Xi ∼ Poisson(Θi). We wish to estimate g(θ) from the observed sample X1, X2, . . . , XN . Traditional asymptotic calculations are discouraging, indicating very slow nonparametric rates of convergence....

September 4, 2009 Version 3.1-94 Date 2009-09-02 Priority recommended Title Linear and Nonlinear Mixed Effects Models Author Jose Pinheiro (S version), Douglas Bates (up to 2007), Saikat DebRoy (up to 2002), Deepayan Sarkar (up to 2005), the R Core team. Maintainer R-core <R-core@R-p...

A shortest-route formulation of the mixed-model assembly line balancing problem is presented. Common tasks across models are assumed to exist and these tasks are performed in the same stations. The formulation is based on an algorithm which solves the single-model version of the problem. The mixed-model system is transformed into a singlemodel system with a combined precedence diagram. The mode...

This paper mainly illustrates how to use PROC MIXED to fit linear mixed models in clinical trials. We first introduce the statistical background of linear mixed models. We explore the situations under which the mixed effects are identified. A randomized complete block design is used to explain the difference between PROC GLM and PROC MIXED in dealing with the linear mixed models. Examples inclu...