A grouping or blocking of observations can be achieved by using categorical or dummy variables. Examples of categorical variables include gender, country of origin, job title and experimental treatment. This should be contrasted with ordinal variables, such as age class, highest degree attained or a score on a 5-point scale with values comprised between strongly agree and strongly disagree. The...

We consider the testing problem in the mixed-effects functional analysis of variance models. We develop asymptotically optimal (minimax) testing procedures for testing the significance of functional global trend and the functional fixed effects based on the empirical wavelet coefficients of the data. Wavelet decompositions allow one to characterize various types of assumed smoothness conditions...

Web-based social networks typically use public trust systems to facilitate interactions between strangers. These systems can be corrupted by misleading information spread under the cover of anonymity, or exhibit a strong bias towards positive feedback, originating from the fear of reciprocity. Trust propagation algorithms seek to overcome these shortcomings by inferring trust ratings between st...

We address the important practical problem of how to select the random effects component in a linear mixed model. A hierarchical Bayesian model is used to identify any random effect with zero variance. The proposed approach reparameterizes the mixed model so that functions of the covariance parameters of the random effects distribution are incorporated as regression coefficients on standard nor...

In mixed effects model, observations are a function of fixed and random effects and an error term. This structure determines a very specific structure for the variances and covariances of these observations. Unfortunately, the specific parameters of this variance/covariance structure might not be identifiable. Nonidentifiability can lead to complications in numerical estimation algorithms or wo...

Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixedand random-effects terms. The formula and data together determine a numerical repr...

We consider the problem of variable selection in general nonlinear mixed-e ets models, including mixed-e ects hidden Markov models. These models are used extensively in the study of repeated measurements and longitudinal analysis. We propose a Bayesian Information Criterion (BIC) that is appropriate for nonstandard situations where both the number of subjects N and the number of measurements pe...

Nonlinear mixed-effects models involve both fixed effects and random effects. Model building for nonlinear mixed-effects models is the process of determining the characteristics of both the fixed and the random effects so as to give an adequate but parsimonious model. We describe procedures based on information criterion statistics for comparing different structures of the random effects compon...

We propose a Bayesian methodology for recommender systems that incorporates user ratings, user features, and item features in a single unified framework. In principle our approach should address the cold-start issue and can address both scalability issues as well as sparse ratings. However, our early experiments have shown mixed results.