Skip to main content

Statistics and Computing

Statistics and Computing
42 Volumes | 1993 - 2019


Statistics and Computing (SC) includes monographs and advanced texts on statistical computing and statistical packages.

All books of the series Statistics and Computing

2019 | Book

Linear Time Series with MATLAB and OCTAVE

This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly …

2018 | Book

Independent Random Sampling Methods

This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored …

2017 | Book

Applied Quantitative Finance

This volume provides practical solutions and introduces recent theoretical developments in risk management, pricing of credit derivatives, quantification of volatility and copula modeling. This third edition is devoted to modern risk analysis …

2017 | Book

Basic Elements of Computational Statistics

This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a strong focus on applications and implementations in the statistical software R. It covers mathematical …

2016 | Book

An Introduction to Statistics with Python

With Applications in the Life Sciences

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from …

2013 | Book

The R Software

Fundamentals of Programming and Statistical Analysis

The contents of The R Software are presented so as to be both comprehensive and easy for the reader to use. Besides its application as a self-learning text, this book can support lectures on R at any level from beginner to advanced. This bo

2012 | Book

Visualizing Time

Designing Graphical Representations for Statistical Data

Art, or Science? Which of these is the right way to think of the field of visualization? This is not an easy question to answer, even for those who have many years experience in making graphical depictions of data with a view to help people unders

2011 | Book

R for SAS and SPSS Users

R is a powerful and free software system for data analysis and graphics, with over 5,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages a

2011 | Book

Evolutionary Statistical Procedures

An Evolutionary Computation Approach to Statistical Procedures Designs and Applications

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and

2010 | Book

R for Stata Users

Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to ext

2010 | Book

A SAS/IML Companion for Linear Models

Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation

2010 | Book

Numerical Analysis for Statisticians

Every advance in computer architecture and software tempts statisticians to tackle numerically harder problems. To do so intelligently requires a good working knowledge of numerical analysis. This book equips students to craft their own software and

2009 | Book

R for SAS and SPSS Users

While SAS and SPSS have many things in common, R is very different. My goal in writing this book is to help you translate what you know about SAS or SPSS into a working knowledge of R as quickly and easily as possible. I point out how they differ usi

2009 | Book

Computational Statistics

Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computationa

2008 | Book

Software for Data Analysis

Programming with R

John Chambers has been the principal designer of the S language since its beginning, and in 1999 received the ACM System Software award for S, the only statistical software to receive this award. He is author or coauthor of the landmark books on S

2008 | Book

SAS for Data Analysis

Intermediate Statistical Methods

This book is intended for use as the textbook in a second course in applied statistics that covers topics in multiple regression and analysis of variance at an intermediate level. Generally, students enrolled in such courses are p- marily graduate ma

2008 | Book

Introductory Statistics with R

R is an Open Source implementation of the well-known S language. It works on multiple computing platforms and can be freely downloaded. R is thus ideally suited for teaching at many levels as well as for practical data analysis and methodological

2006 | Book

Graphics of Large Datasets

Visualizing a Million

Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day? This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases or large in numbers

2005 | Book

The Grammar of Graphics

Preface to First Edition Before writing the graphics for SYSTAT in the 1980’s, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find. I was determined to produce a package that co

2005 | Book

Branch-and-Bound Applications in Combinatorial Data Analysis

This monograph focuses on the application of the solution strategy known as branch-and-bound to problems of combinatorial data analysis. Combinatorial data analysis problems typically require either the sel- tion of a subset of objects from a larger