The hierarchical normal-normal model considered. Standard Empirical Bayes methods underestimate variability because they ignore uncertainty about the hyperparameters. Bayes' theorem solves this problem. We provide fast, exact inference that requires only a simple, univariate numerical integration to obtain the posterior distribution of the means. However, when standard, scale-invariant, vague p...

In this letter, a Hierarchical Parametric Empirical Bayes model is proposed to model spike count data. We have integrated Generalized Linear Models (GLMs) and empirical Bayes theory to simultaneously provide three advantages: (1) a model of over-dispersion of spike count values; (2) reduced MSE in estimation when compared to using the maximum likelihood method for GLMs; and (3) an efficient alt...

In the analysis of genome-wide association (GWA) data, the aim is to detect statistical associations between single nucleotide polymorphisms (SNPs) and the disease or trait of interest. These SNPs, or the particular regions of the genome they implicate, are then considered for further study. We demonstrate through a comprehensive simulation study that the inclusion of additional, biologically r...

BACKGROUND Leukocyte telomere length (LTL) is considered a biomarker of human aging and based on cross-sectional studies it shortens with age. However, longitudinal studies reported that many adults display LTL lengthening. METHODS Using Southern blots, we compared cross-sectional rates of age-related LTL change across a ∼20 year age range with those based on longitudinal evaluations in three...

Quantitative training for students of ecology has traditionally emphasized two sets of topics: mathematical modeling and statistical analysis. Until recently, these topics were taught separately, modeling courses emphasizing mathematical techniques for symbolic analysis and statistics courses emphasizing procedures for analyzing data. We advocate the merger of these traditions in ecological edu...

Over the past decade, meta-analyses have played an increasingly important role in summarizing clinical information and informing policy. Consequently, a new generation of meta-analysts have come forth and the demand for a streamlined modeling approach has emerged. In particular, compared to linear model estimation, many aspects of selecting and tting models for meta-analysis involve nonstandard...