Many observational studies have shown an association between income and health. However, much less research has assessed how changes in income influence health over time. This could help illuminate the issue of whether income is causally related to health, if longitudinal survey data were analysed with methods to account for bias from both measured and unmeasured time-invariant confounders. The...

Data from 82 studies (60 steer and 22 heifer studies) were compiled and analyzed to evaluate the effects of anabolic implants on feedlot performance and carcass traits. Dependent variables in the model included ADG, G:F, DMI, dressing percentage, HCW, and marbling score. Categories created for type and dosage of active compound were: low dose primarily estrogenic hormone (E2; LOW), moderate dos...

Many exposure assessment strategies rely on the occupational group as the unit of analysis in which workers are classified on the basis of job title, location, or on other characteristics related to the workplace or the job. Although statistical methods that combine exposure data collected on workers from different occupational groups are more efficient, the underlying assumption that the degre...

In this paper, we consider semiparametric estimation in a partially linear single– index panel data model with fixed effects. Without taking the difference explicitly, we propose using a semiparametric minimum average variance estimation (SMAVE) based on a dummy–variable method to remove the fixed effects and obtain consistent estimators for both the parameters and the unknown link function. As...

Motivated by the need of estimating the main spawning period of North Sea cod, we develop a common transfer function model with a panel of contemporaneously correlated times series data. This model incorporates (i) the smoothness on the parameters by assuming that the second differences are small and (ii) the contemporaneous correlation by assuming that the errors have a general variance-covari...

This paper considers ways to increase computational speed in generalized linear mixed pseudo-models for the case of many repeated measurements on subjects. We obtain linearly increasing computing timewith number of observations, as opposed to O(n3) increasing computing time using numerical optimization. We also find a surprising result; that incomplete optimization for covariance parameterswith...

We propose a mixed-effects vector auto-regressive (ME-VAR) model for studying brain effective connectivity. One common approach to investigating inter-regional associations in brain activity is the multivariate auto-regressive (VAR) model. The standard VAR model unrealistically assumes the connectivity structure to be identical across all participants in a study and therefore, could yield misle...

The practice of dynamical modeling of perception-action behavior has lagged behind the proliferation of the dynamical perspective. Two methodological roadblocks to dynamical modeling are discussed. First, parameter selection is difficult with current tools. Second, it is unclear what role models have in the larger scientific project beyond their use as descriptions or proofs of concept. In this...

Generalized linear mixed-effect models are widely used for the analysis of correlated nonGaussian data such as those found in longitudinal studies. In this article, we consider extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method, and our focus is on the efficient computation and the effective smoothing parameter se...

Of widespread interest in social science are observational studies in which entities (persons, schools, states, countries, etc.) are exposed to varied treatment conditions over time. As in all observational studies, the non-randomized assignment of treatments poses challenges to valid causal inference. An attractive feature of panel studies with time-varying treatments, however, is that the des...