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.

Does health affect portfolio choice

Abstract: A number of recent studies find that poor health is empirically associated with a safer portfolio allocation. It is difficult to say, however, whether this relationship is truly causal. Both health status and portfolio choice are influenced by unobserved characteristics such as risk attitudes, impatience, information, and motivation, and these unobserved factors, if not adequately controlled for, can induce significant bias in the estimates of asset demand equations. Using the 1992–2006 waves of the Health and Retirement Study, we investigate how much of the connection between health and portfolio choice is causal and how much is due to the effects of unobserved heterogeneity. Accounting for unobserved heterogeneity with fixed effects and correlated random effects models, we find that health does not appear to significantly affect portfolio choice among single households. For married households, we find a small effect (about 2–3 percentage points) from being in the lowest of five self-reported health categories. Copyright © 2009 John Wiley & Sons, Ltd.

Bayesian Tobit Modeling of Longitudinal Ordinal Clinical Trial Compliance Data with Nonignorable Missingness

Abstract: In the Lung Health Study (LHS), compliance with the use of inhaled medication was assessed at each follow-up visit both by self-report and by weighing the used medication canisters. One or both of these assessments were missing if the participant failed to attend the visit or to return all canisters. Approximately 30% of canister-weight data and 5% to 15% of self-report data were missing at different visits. We use Gibbs sampling with data augmentation and a multivariate Hastings update step to implement a Bayesian hierarchical model for LHS inhaler compliance. Incorporating individual-level random effects to account for correlations among repeated measures on the same participant, our model is a longitudinal extension of the Tobit models used in econometrics to deal with partially unobservable data. It enables (a) assessment of the relationships among visit attendance, canister return, self-reported compliance level, and canister weight compliance, and (b) determination of demographic, physiolog...

Prediction Assessment of Shrinkage Estimators of Multiplicative Models for Multi-Environment Cultivar Trials

Abstract: Multiplicative statistical models such as the additive main effects and multiplicative interaction model (AMMI), the genotypes regression model (GREG), the sites regression model (SREG), the completely multiplicative model (COMM), and the shifted multiplicative model (SHMM) are useful for studying patterns of yield response across sites and estimating realized cultivar responses in specific environments Traditionally the series of multiplicative terms is truncated at some point beyond which further terms are believed to have little statistical significance or predictive value. Shrinkage estimators have been advocated as a model fitting method superior to model truncation. In this study, by data splitting and cross validation, we evaluated the predictive accuracy of (i) truncated multiplicative models, (ii) shrinkage estimators of multiplicative models, (iii) Best Linear Unbiased Predictors (BLUP) of the cell means based on a two-way random effects model with interaction, and (iv) empirical cell means in one wheat [durum (Triticum turgidum L. var. durum) and bread (Triticum aestivum L.)] and four maize (Zea mays L.) cultivar trials, with and without adjustment for replicate differences within environments. Shrinkage estimates of multiplicative models were at least as good as the better choice of truncated models fitted by least squares or BLUPs. Shrinkage estimation yields potentially better estimates of cultivar performance than do truncated multiplicative models and eliminates the need for cross validation or tests of hypotheses as criteria for determining the number of multiplicative terms to be retained. If random cross validation is used to choose a truncated model, data should be adjusted for replicate differences within environments.

A Structural Modeling Approach to a Multilevel Random Coefficients Model

Abstract: A method for estimating the random coefficients model using covariance structure modeling is presented. This method allows one to estimate both fixed and random effects. A way of translating the general linear mixed model into a structural equation modeling (SEM) format is presented. In particular, a LISREL setup for the multiple group linear latent growth curve model is illustrated with suggestions on ways to parameterize more complex models. To illustrate the procedure, we apply the method to both simulated and real data. The method is shown to recover the simulated parameter values. Results and interpretation for the Belsky and Rovine (1990) marriage data are presented. Other applications of the more general model are suggested.