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Karen Kafadar

Publications and source records attributed to Karen Kafadar.

3 recordsLinked to original sources
arXiv2012

Special section: Statistical methods for next-generation gene sequencing data

This issue includes six articles that develop and apply statistical methods for the analysis of gene sequencing data of different types. The methods are tailored to the different data types and, in each case, lead to biological insights not readily identified without the use of statistical methods. A common feature in all articles is the development of methods for analyzing simultaneously data of different types (e.g., genotype, phenotype, pedigree, etc.); that is, using data of one type to inform the analysis of data from another type.

stat.AP↗
arXiv2012

Special section on modern multivariate analysis

A critically challenging problem facing statisticians is the identification of a suitable framework which consolidates data of various types, from different sources, and across different time frames or scales (many of which can be missing), and from which appropriate analysis and subsequent inference can proceed.

stat.AP↗
arXiv2011

Special section on statistics in neuroscience

This article provides a brief introduction to seven papers that are included in this special section on Statistics in Neuroscience: (1) Xiaoyan Shi, Joseph G. Ibrahim, Jeffrey Lieberman, Martin Styner, Yimei Li and Hongtu Zhu: Two-state empirical likelihood for longitudinal neuroimaging data (2) Vincent Q. Vu, Pradeep Ravikumar, Thomas Naselaris, Kendrick N. Kay, Jack L. Gallant and Bin Yu: Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models (3) Sourabh Bhattacharya and Ranjan Maitra: A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments (4) Christopher J. Long, Patrick L. Purdon, Simona Temereanca, Neil U. Desai, Matti S. Hämäläinen and Emery Neal Brown: State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing (5) Yuriy Mishchencko, Joshua T. Vogelstein and Liam Paninski: A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data (6) Robert E. Kass, Ryan C. Kelly and Wei-Liem Loh: Assessment of synchrony in multiple neural spike trains using loglinear point process models (7) Sofia Olhede and Brandon Whitcher: Nonparametric tests of structure for high angular resolution diffusion imaging in Q-space

stat.AP↗