Forthcoming

**Annette J. Dobson, Adrian Barnett**

May 04, 2018

This is the fourth edition of a hugely successful textbook on Generalized Linear Models (GLMs). It has been adopted around the world in various departments, including statistics, public health, social sciences, etc. The new edition has been updated with a new chapter on GLMs for Big Data, and other...

Forthcoming

**Kevin J. Keen**

April 25, 2018

Praise for the First Edition "The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data. In addition, it is clearly written in plain language and the inclusion of R code is...

Forthcoming

**David A. Harville**

March 01, 2018

Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It can be a valuable reference, a primary or secondary text in a graduate-level course on linear models...

**Athanasios Christou Micheas**

January 23, 2018

This book defines and investigates the concept of a random object. To accomplish this task in a natural way, it brings together three major areas; statistical inference, measure-theoretic probability theory and stochastic processes. This point of view has not been explored by existing textbooks;...

**Peter Watts Jones, Peter Smith**

October 16, 2017

Based on a well-established and popular course taught by the authors over many years, Stochastic Processes: An Introduction, Third Edition, discusses the modelling and analysis of random experiments, where processes evolve over time. The text begins with a review of relevant fundamental probability...

**Piotr Kokoszka, Matthew Reimherr**

August 09, 2017

Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework....

**Norman Matloff**

August 01, 2017

Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough...

**Thomas A Severini**

July 12, 2017

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4...

**Max Morris**

May 31, 2017

Offering deep insight into the connections between design choice and the resulting statistical analysis, Design of Experiments: An Introduction Based on Linear Models explores how experiments are designed using the language of linear statistical models. The book presents an organized framework for...

**Simon N. Wood**

May 30, 2017

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary...

**Joseph M. Hilbe**

May 25, 2017

Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The...

**Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton**

February 02, 2017

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical...