Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more.
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Table of Contents
- Markov chain Monte Carlo methods: Theory and practiceDavid A. Spade
- An information and statistical analysis pipeline for microbial metagenomic sequencing data
Shinji Nakaoka and Keisuke Ohta
- Machine learning algorithms, applications, and practices in data science
Kalidas Yeturu
- Bayesian model selection for high-dimensional data
Naveen Naidu Narisetty
- Competing risks: Aims and methods
Ronald Geskus
- High-dimensional statistical inference: Theoretical development to data analytics
Deepak Nag Ayyala
- Big data challenges in genomics
Hongyan Xu
- Analysis of microarray gene expression data using information theory and stochastic algorithm
Narayan Behera
- Human life expectancy is computed from an incomplete sets of data: Modeling and analysis
Arni S.R. Srinivasa Rao and James R. Carey
- Support vector machines: A robust prediction method with applications in bioinformatics
Arnout Van Messem
