Big and Complex Data Analysis Methodologies and Applications /
This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essent...
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| Language: | English |
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Cham :
Springer International Publishing : Imprint: Springer,
2017.
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| Edition: | 1st ed. 2017. |
| Series: | Contributions to Statistics,
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| Online Access: | https://doi.org/10.1007/978-3-319-41573-4 |
Table of Contents:
- Preface
- Introduction
- Unsupervised Bump Hunting Using Principal Components
- Statistical Process Control Charts as a Tool for Analyzing Big Data
- Empirical Likelihood Test for High Dimensional Generalized Linear Models
- Identifying gene-environment interactions associated with prognosis using penalized quantile regression
- A Computationally Efficient Approach for Modeling Complex and Big Survival Data
- Regularization after marginal learning for ultra-high dimensional regression models
- Tests of concentration for low-dimensional and high-dimensional directional data
- Random Projections For Large-Scale Regression
- How Different are Estimated Genetic Networks of Cancer Subtypes?
- Analysis of correlated data with error-prone response under generalized linear mixed models
- High-Dimensional Classification for Brain Decoding
- Optimal shrinkage estimation in heteroscedastic hierarchical linear models
- Bias-reduced moment estimators of Population Spectral Distribution and their applications
- Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values
- A Mixture of Variance-Gamma Factor Analyzers
- Fast Community Detection in Complex Networks with a K-Depths Classifier.