Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors ...

Neuroimaging studies aim to analyze imaging data with complex spatial patterns in a large number of locations (called voxels) on a two-dimensional (2D) surface or in a 3D volume. Conventional analyses of imaging data include two sequential steps: spatially smoothing imaging data and then independently fitting a statistical model at each voxel. However, conventional analyses suffer from the same...

Recent controversy over the disagreement of population attributable fraction estimates for the obesity-total mortality relation has made the concept of attributable fraction visible in both scientific and popular news. Most of the attention in writings on the attributable fraction has focused on technical matters of estimation and on ensuring a causal relationship between exposure and outcome. ...

The paper provides an introduction to and survey of probabilistic approaches to modelling Information Retrieval. The basic concepts of probabilistic approaches to Information Retrieval are outlined, and the principles and assumptions upon which the approaches are based are presented. The various models that have been proposed in the development of IR are described, classi ed, and compared. The ...

Methods of risk assessment for alien species, especially for nonagricultural systems, are largely qualitative. Using a generalizable risk assessment approach and statistical models of fish introductions into the Great Lakes, North America, we developed a quantitative approach to target prevention efforts on species most likely to cause damage. Models correctly categorized established, quickly s...

BACKGROUND Attrition, which is virtually ubiquitous in randomized controlled clinical trials, introduces problems of increased bias and reduced statistical power. Although likelihood-based statistical models such as mixed-effects models can accommodate incomplete data, the assumption of ignorable attrition is usually required for valid inferences. PURPOSE In an effort to make the ignorability...

Floods continue to inflict the most damage upon human communities among all natural hazards in the United States. Because localized flooding tends to be spatially repetitive over time, local decisionmakers often have an opportunity to learn from previous events and make proactive policy adjustments to reduce the adverse effects of a subsequent storm. Despite the importance of understanding the ...

A detailed comparison of statistical models based on the quasiclassical trajectory (SQCT) and quantum mechanical (SQM) methods is presented in this work for the C((1)D)+H(2), S((1)D)+H(2), O((1)D)+H(2) and N((2)D)+H(2) insertion reactions. Reaction probabilities, integral (ICS) and differential (DCS) cross sections at different levels of product's state resolution are shown and discussed for th...

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describes [corrected] 4 application...

High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern stat...