Title: Principles and methods for data science /
Authors: Srinivasa Rao, Arni S. R.; Rao, C. R.
Year: 2020
Subjects: Data mining Methods.; Big data Methods.; Quantitative research; Markov processes; Monte Carlo method; Bayesian statistical decision theory; Databases; Bayesian statistical decision theory; Big data; Data mining; Databases; Markov processes; Monte Carlo method; Quantitative research
Abstract: 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.
Contents: Markov chain Monte Carlo methods: theory and practice / David A. Spade -- Information and statistical analysis pipeline for microbial metagenomic sequencing data / Shinji Nakaoka and Keisuke H. Ota -- Machine learning algorithms, applications, and practices in data science / Kalidas Yeturu -- Bayesian model selectiong for high-dimensional data / Naveen Naidu Narisetty -- Competing risks: aims and methods / Ronald B. 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.