Principles and Methods for Data Science

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.

Provides the authority and expertise of leading contributors from an international board of authorsPresents the latest release in the Handbook of Statistics seriesUpdated release includes the latest information on Principles and Methods for Data Science

1. Markov chain Monte Carlo methods: Theory and practice

David A. Spade

2. An information and statistical analysis pipeline for microbial metagenomic sequencing data

Shinji Nakaoka and Keisuke Ohta

3. Machine learning algorithms, applications, and practices in data science

Kalidas Yeturu

4. Bayesian model selection for high-dimensional data

Naveen Naidu Narisetty

5. Competing risks: Aims and methods

Ronald Geskus

6. High-dimensional statistical inference: Theoretical development to data analytics

Deepak Nag Ayyala

7. Big data challenges in genomics

Hongyan Xu

8. Analysis of microarray gene expression data using information theory and stochastic algorithm

Narayan Behera

9. Human life expectancy is computed from an incomplete sets of data: Modeling and analysis

Arni S.R. Srinivasa Rao and James R. Carey

10. Support vector machines: A robust prediction method with applications in bioinformatics

Arnout Van Messem