ACCF TASK FORCE MEMBERS Robert A. Harrington, MD, FACC, Chair; Eric R. Bates, MD, FACC; Charles R. Bridges, MD, MPH, FACC; Mark J. Eisenberg, MD, MPH, FACC; Victor A. Ferrari, MD, FACC; Mark A. Hlatky, MD, FACC; Sanjay Kaul, MBBS, FACC; Jonathan R. Lindner, MD, FACC‡; David J. Moliterno, MD, FACC; Debabrata Mukherjee, MD, FACC; Richard S. Schofield, MD, FACC‡; Robert S. Rosenson, MD, FACC; Jame...

Decisions or inferences sometimes have to made in situations where substantive information about aspects of the problem are either lacking or conflicting. This is often handled by constructing a non-informative prior by appealing to principles such as indifference, maximum entropy, invariance, or maximizing missing information. Unfortunately these priors and the resulting posteriors may depend ...

Motivated by a climate prediction problem, we consider high dimensional Bayesian regression where the number of covariates is much larger than the number of observations. To reduce the dimension of the covariate, the response is regressed on the principal components obtained from the covariates, and it is argued that the PCA regression is equivalent to the original model in terms of prediction....

A problem of classification of local field potentials (LFPs), recorded from the prefrontal cortex of a macaque monkey, is considered. An adult macaque monkey is trained to perform a memory based saccade. The objective is to decode the eye movement goals from the LFP collected during a memory period. The LFP classification problem is modeled as that of classification of smooth functions embedded...

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networ...

We study orthogonal decomposition of symmetric statistics based on samples drawn without replacement from finite populations. Under very mild smoothness conditions the first k terms of the decomposition provide stochastic expansion with remainder O(N−k/2). Assuming that the linear part of the decomposition is nondegenerate we establish one term Edgeworth expansion of the distribution function o...

For general estimable parameters in a nonparametric setup, shrinkage (Stein-rule) and preliminary test estimator versions of U-statistics are considered for the (multi-parameter) minimum risk sequential estimation problem. In the usual fashion, allowing the cost per unit sample to be small, an asymptotic model is framed, and in this setup, the asymptotic distributional risks of these versions o...