Forthcoming

By **Sergio Rey, Dani Arribas-Bel, Levi John Wolf**

May 10, 2023

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in ...

Forthcoming

By **Wan Tang, Hua He, Xin M. Tu**

April 18, 2023

Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis, Second Edition explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors have been teaching categorical data analysis ...

Forthcoming

By **David Collett**

April 17, 2023

Modelling Survival Data in Medical Research, Fourth Edition describes the analysis of survival data, illustrated using a wide range of examples from biomedical research. Written in a non-technical style, it concentrates on how the techniques are used in practice. Starting with standard methods for ...

Forthcoming

By **Steffen Lauritzen**

February 08, 2023

Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate level course on Mathematical Statistics. It covers all the key topics - statistical models, linear normal models, exponential families, estimation, asymptotics of maximum ...

Forthcoming

By **Felix Abramovich, Ya'acov Ritov**

December 23, 2022

Designed for a one-semester advanced undergraduate or graduate statistical theory course, Statistical Theory: A Concise Introduction, Second Edition clearly explains the underlying ideas, mathematics, and principles of major statistical concepts, including parameter estimation, confidence intervals...

Forthcoming

By **Frans E.S. Tan, Shahab Jolani**

December 09, 2022

This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract ...

By **Miltiadis C. Mavrakakis, Jeremy Penzer**

September 26, 2022

Probability and Statistical Inference: From Basic Principles to Advanced Models covers aspects of probability, distribution theory, and inference that are fundamental to a proper understanding of data analysis and statistical modelling. It presents these topics in an accessible manner without ...

By **Wayne A. Woodward, Bivin Philip Sadler, Stephen Robertson**

August 01, 2022

Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. ...

By **Bryan F.J. Manly, Jorge A. Navarro Alberto**

April 29, 2022

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical ...

By **Nathan Taback**

April 27, 2022

Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data ...

By **Alicia A. Johnson, Miles Q. Ott, Mine Dogucu**

March 04, 2022

An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced ...

By **Olga Korosteleva**

February 17, 2022

Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is...