This book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems.

The book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented.

This book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in courses which aim to provide an overview of modern statistics and its applications.

Introduces a comprehensive collection of topics in modern statistical areas Presents applications to topics in genetics and environmental science Suitable for upper undergraduate and graduate students as well as researchers

Autorentext

Mayer Alvo is a Professor in the Department of Mathematics and Statistics at the University of Ottawa. He received his Ph.D. from Columbia University in 1972. He served as Departmental Chairman in 1985-88, 2002- 2005 and 2011-2012. He is the author of more than 64 articles published in refereed journals. His research interests include nonparametric statistics, Bayesian analysis and sequential methods. Philip L.H. Yu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong. He received his Ph.D. from The University of Hong Kong in 1993. He is the Director of the Master of Statistics Programme. He is an Associate Editor for Computational Statistics and Data Analysis as well as for Computational Statistics. He is the author of more than 90 referred publications. His research interests include modeling of ranking data, data mining and financial and risk analytics.

Klappentext

This book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems. The book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented. This book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in courses which aim to provide an overview of modern statistics and its applications.

Inhalt
I. Introduction to Big Data.- Examples of Big Data.- II. Statistical Inference for Big Data.- Basic Concepts in Probability.- Basic Concepts in Statistics.- Multivariate Methods.- Nonparametric Statistics.- Exponential Tilting and its Applications.- Counting Data Analysis.- Time Series Methods.- Estimating Equations.- Symbolic Data Analysis.- III Machine Learning for Big Data.- Tools for Machine Learning.- Neural Networks.- IV Computational Methods for Statistical Inference.- Bayesian Computation Methods.