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Statistical Analysis for High-Dimensional Data

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Cover of 'Statistical Analysis for High-Dimensional Data'

Table of Contents

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    Book Overview
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    Chapter 1 Some Themes in High-Dimensional Statistics
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    Chapter 2 Laplace Approximation in High-Dimensional Bayesian Regression
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    Chapter 3 Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration
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    Chapter 4 Spectral Clustering and Block Models: A Review and a New Algorithm
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    Chapter 5 Bayesian Hierarchical Mixture Models
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    Chapter 6 iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH Data
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    Chapter 7 Models of Random Sparse Eigenmatrices and Bayesian Analysis of Multivariate Structure
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    Chapter 8 Combining Single and Paired End RNA-seq Data for Differential Expression Analyses
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    Chapter 9 An Imputation Method for Estimating the Learning Curve in Classification Problems
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    Chapter 10 Bayesian Feature Allocation Models for Tumor Heterogeneity
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    Chapter 11 Bayesian Penalty Mixing: The Case of a Non-separable Penalty
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    Chapter 12 Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data
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    Chapter 13 χ 2 -Confidence Sets in High-Dimensional Regression
Attention for Chapter 8: Combining Single and Paired End RNA-seq Data for Differential Expression Analyses
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Chapter title
Combining Single and Paired End RNA-seq Data for Differential Expression Analyses
Chapter number 8
Book title
Statistical Analysis for High-Dimensional Data
Published by
Springer, Cham, January 2016
DOI 10.1007/978-3-319-27099-9_8
Book ISBNs
978-3-31-927097-5, 978-3-31-927099-9
Authors

Zhi-Ping Feng, Francois Collin, Terence P. Speed, Feng, Zhi-Ping, Collin, Francois, Speed, Terence P.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 100%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 100%

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