【latex版】水贴

Youngest Boston Marathon victim immortalized with statue
USA TODAY
BOSTON — When creating a bronze statue of a person, etching out the smile can be a bit problematic. But when artist Victoria Guerina set out to make the life-size statue of 8-year-old Martin Richard, — unveiled at Bridgewater State University on

Florida woman charged with felony for allegedly riding sea turtle
Reuters
Sept 26 A 20-year-old Florida woman was arrested on Saturday on suspicion of riding a sea turtle on the beach in photos that went viral on social media in early July, police said. Stephanie Moore, of Melbourne, faces a felony charge of possessing ...

Cuba's Castro slams US trade embargo at United Nations
Reuters
UNITED NATIONS Cuba's President Raul Castro, speaking at the United Nations for the first time, on Saturday hailed renewed diplomatic ties with the United States as "major progress" but slammed a U.S. trade embargo as the top obstacle to Cuba's ...

New York Times       
As Volkswagen Pushed to Be No. 1, Ambitions Fueled a Scandal
New York Times
Martin Winterkorn, Volkswagen's chief executive, took the stage four years ago at the automaker's new plant in Chattanooga, Tenn., and outlined a bold strategy. The company, he said, was in the midst of a plan to more than triple its sales in the ...

Designs for Learning Environments of the Future: International Perspectives from the Learning Sciences by Michael Jacobson
English | Feb. 19, 2010 | ISBN: 0387882782 | 296 Pages | PDF | 6 MB

Sparse meta-analysis with high-dimensional data

Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiple studies. With high-dimensional data, the incorporation of variable selection into meta-analysis improves model interpretation and prediction. Existing variable selection methods require direct access to raw data, which may not be available in practical situations. We propose a new approach, sparse meta-analysis (SMA), in which variable selection for meta-analysis is based solely on summary statistics and the effect sizes of each covariate are allowed to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also show that our approach achieves selection consistency and estimation consistency even when summary statistics include only the variance estimators or no variance/covariance information at all. Simulation studies and applications to high-throughput genomics studies demonstrate the usefulness of our approach.

Key words
Fixed-effects models Genomics studies Oracle property Random-effects models Variable selection Within-group sparsity

Protein sequence data arise more and more often in vaccine and infectious disease research. These types of data are discrete, high-dimensional, and complex. We propose to study the impact of protein sequences on binary outcomes using a kernel-based logistic regression model, which models the effect of protein through a random effect whose variance–covariance matrix is mostly determined by a kernel function. We propose a novel, biologically motivated, profile hidden Markov model (HMM)-based mutual information (MI) kernel.

Modeling agreement on categorical scales in the presence of random scorers