Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

A Semiparametric Bayesian Approach to the Random Effects Model

Abstract: In longitudinal random effects models, the random effects are typically assumed to have a normal distribution in both Bayesian and classical models. We provide a Bayesian model that allows the random effects to have a nonparametric prior distribution. We propose a Dirichlet process prior for the distribution of the random effects; computation is made possible by the Gibbs sampler. An example using marker data from an AIDS study is given to illustrate the methodology.

RE-EM trees: a data mining approach for longitudinal and clustered data

Abstract: Longitudinal data refer to the situation where repeated observations are available for each sampled object. Clustered data, where observations are nested in a hierarchical structure within objects (without time necessarily being involved) represent a similar type of situation. Methodologies that take this structure into account allow for the possibilities of systematic differences between objects that are not related to attributes and autocorrelation within objects across time periods. A standard methodology in the statistics literature for this type of data is the mixed effects model, where these differences between objects are represented by so-called "random effects" that are estimated from the data (population-level relationships are termed "fixed effects," together resulting in a mixed effects model). This paper presents a methodology that combines the structure of mixed effects models for longitudinal and clustered data with the flexibility of tree-based estimation methods. We apply the resulting estimation method, called the RE-EM tree, to pricing in online transactions, showing that the RE-EM tree is less sensitive to parametric assumptions and provides improved predictive power compared to linear models with random effects and regression trees without random effects. We also apply it to a smaller data set examining accident fatalities, and show that the RE-EM tree strongly outperforms a tree without random effects while performing comparably to a linear model with random effects. We also perform extensive simulation experiments to show that the estimator improves predictive performance relative to regression trees without random effects and is comparable or superior to using linear models with random effects in more general situations.

Likelihood based inference for skew-normal independent linear mixed models

Abstract: Linear mixed models with normally distributed response are routinely used in longitudinal data. However, the accuracy of the assumed normal distribu- tion is crucial for valid inference of the parameters. We present a new class of asym- metric linear mixed models that provides for an efficient estimation of the parame- ters in the analysis of longitudinal data. We assume that, marginally, the random effects follow a multivariate skew-normal/independent distribution (Branco and Dey (2001)) and that the random errors follow a symmetric normal/independent distribution (Lange and Sinsheimer (1993)), providing an appealing robust alter- native to the usual symmetric normal distribution in linear mixed models. Specific distributions examined include the skew-normal, the skew-t, the skew-slash, and the skew-contaminated normal distribution. We present an efficient EM-type algo- rithm for the computation of maximum likelihood estimation of parameters. The technique for the prediction of future responses under this class of distributions is also investigated. The methodology is illustrated through an application to Fram- ingham cholesterol data and a simulation study.

Neither fixed nor random: weighted least squares meta-analysis

Abstract: This study challenges two core conventional meta-analysis methods: fixed effect and random effects. We show how and explain why an unrestricted weighted least squares estimator is superior to conventional random-effects meta-analysis when there is publication (or small-sample) bias and better than a fixed-effect weighted average if there is heterogeneity. Statistical theory and simulations of effect sizes, log odds ratios and regression coefficients demonstrate that this unrestricted weighted least squares estimator provides satisfactory estimates and confidence intervals that are comparable to random effects when there is no publication (or small-sample) bias and identical to fixed-effect meta-analysis when there is no heterogeneity. When there is publication selection bias, the unrestricted weighted least squares approach dominates random effects; when there is excess heterogeneity, it is clearly superior to fixed-effect meta-analysis. In practical applications, an unrestricted weighted least squares weighted average will often provide superior estimates to both conventional fixed and random effects.