Statistical analysis for high dimensional data: the abel symposium 2014
Material type:
- 9783319270975
- 519.5 S8
Item type | Current library | Item location | Collection | Shelving location | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|---|
Books | Vikram Sarabhai Library | Rack 28-B / Slot 1416 (0 Floor, East Wing) | Non-fiction | General Stacks | 519.5 S8 (Browse shelf(Opens below)) | Available | 192868 |
Table of contents:
1.Some Themes in High-Dimensional Statistics
2.Laplace Approximation in High-Dimensional Bayesian
Regression
3.Preselection in Lasso-Type Analysis for Ultra-High
Dimensional Genomic Exploration
4.Spectral Clustering and Block Models: A Review and a New Algorithm
5.Bayesian Hierarchical Mixture Models
6.iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH Data
7.Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure
8.Combining Single and Paired End RNA-seq Data for Differential Expression Analyses
9.An Imputation Method for Estimating the Learning Curve in Classification Problems
10.Bayesian Feature Allocation Models for Tumor Heterogeneity
11.Bayesian Penalty Mixing: The Case of a Non-separable Penalty
12.Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data
13.X2-Confidence sets in high-dimensional regression
This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014.
The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection.
Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.
http://www.springer.com/gp/book/9783319270975
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