天美传媒

The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these 鈥渂ig data鈥� that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. 

Spatio-Temporal Statistics with R by Christopher K. Wikle, Andrew Zammit-Mangion, and Noel Cressie, provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book:

• Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modeling, with an emphasis on hierarchical statistical models and basis-function expansions, and finishing with model evaluation.
• Provides a gradual entry to the methodological aspects of spatio-temporal statistics.
• Provides broad coverage of using R as well as 鈥淩 Tips鈥� throughout.
• Features detailed examples and applications in end-of-chapter Labs.
• Features 鈥淭echnical Notes鈥� throughout to provide additional technical detail where relevant.

The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.

Wikle, Zammit-Mangion and Cressie (2019) is also available as a free downloadable PDF at . The website is meant to serve several purposes: It is a landing page for the book (including an associated R package STRbook); it is a place where new software, data sets, and articles on spatio-temporal statistics can be posted; and it gives publisher details where a hard-cover version can purchased from C&H/CRC Press.

Christopher K. Wikle is Curators鈥� Distinguished Professor and Chair of the Department of Statistics at the University of Missouri, USA.

Andrew Zammit-Mangion is a Discovery Early Career Researcher Award (DECRA) Fellow and Senior Lecturer in the School of Mathematics and Applied Statistics at the University of 天美传媒, Australia.

Noel Cressie, FAA is Distinguished Professor in the School of Mathematics and Applied Statistics and Director of the Centre for Environmental Informatics at the University of 天美传媒, Australia.

Ken Russell, honorary professor in NIASRA, has written a book on the design of experiments when the data to be collected will be analysed by a generalized linear model (GLM). The book concentrates on situations where the predictor variables are 鈥榠nterval鈥� or 鈥榬atio鈥� in nature.

There are numerous books on the analysis of data by GLMs, but it is believed that this is the first book written solely on the topic of design. The target audience includes scientists as well as statisticians. Unlike much of the other material on this topic, it is not assumed that the reader has a mathematics background roughly equivalent to Honours level. Programs in R to perform the necessary calculations are included in the text or are available on a website of supporting material.

Chapter headings are 1. Generalized Linear Models; 2. Background Material; 3. The Theory Underlying Design; 4. The Binomial Distribution; 5. The Poisson Distribution; 6. Several Other Distributions; 7. Bayesian Experimental Design.

鈥淒esign of Experiments for Generalized Linear Models鈥� has been published by CRC Press in its Chapman & Hall/ CRC Press Interdisciplinary Statistics. For more details see .

Distinguished Professor Noel Cressie contributed the chapter Environmental informatics: Uncertainty quantification in the environmental sciences.

Past, Present, and Future of Statistical Science was commissioned in 2013 by the Committee of Presidents of Statistical Societies (COPSS) to celebrate its 50th anniversary and the International Year of Statistics. COPSS consists of five charter member statistical societies in North America and is best known for sponsoring prestigious awards in statistics, such as the COPSS Presidents鈥� award.

Through the contributions of a distinguished group of 50 statisticians who are past winners of at least one of the five awards sponsored by COPSS, this volume showcases the breadth and vibrancy of statistics, describes current challenges and new opportunities, highlights the exciting future of statistical science, and provides guidance to future generations of statisticians. The book is not only about statistics and science but also about people and their passion for discovery.

Distinguished authors present expository articles on a broad spectrum of topics in statistical education, research, and applications. Topics covered include reminiscences and personal reflections on statistical careers, perspectives on the field and profession, thoughts on the discipline and the future of statistical science, and advice for young statisticians. Many of the articles are accessible not only to professional statisticians and graduate students but also to undergraduate students interested in pursuing statistics as a career and to all those who use statistics in solving real-world problems. A consistent theme of all the articles is the passion for statistics enthusiastically shared by the authors. Their success stories inspire, give a sense of statistics as a discipline, and provide a taste of the exhilaration of discovery, success, and professional accomplishment.

Further details are available at .

NIASRA members Ray Chambers and David Steel have collaborated with colleagues Suojin Wang from Texas A&M University and Alan Welsh from the Australian National University to produce a book on Maximum Likelihood Estimation for Sample Surveys, which has recently been published by CRC press.

Sample surveys provide data used by researchers in a large range of disciplines to analyse important relationships using well-established and widely-used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types including multilevel data, and is illustrated by many worked examples using tractable and widely-used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling.

Further details are available at .

Ray Chambers and Robert Clark, have published a new book titled "An Introduction to Model-Based Survey Sampling with Applications".

This text brings together important ideas on the model-based approach to sample survey, which has been developed over the last twenty years. Suitable for graduate students and professional statisticians, it moves from basic ideas fundamental to sampling to more rigorous mathematical modelling and data analysis and includes exercises and solutions.

 further details of the book, or  the book.

Released in 2011, Distinguished Professor Noel Cressie in conjunction with Christopher K. Wikle published "Statistics for Spatio-Temporal Data", a major text in the sphere of Spatial Statistics and Environmental Statistics. The book won the 2011 PROSE Award for Professional and Scholarly Excellence in the Mathematics Category, from the Association of American Publishers.

The book incorporates ideas from the areas of time series and spatial statistics as well as stochastic processes. Beginning with separate treatments of temporal data and spatial data, the book combines these concepts to discuss spatio-temporal statistical methods for understanding complex processes.

This is a state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods. From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account. Statistics for Spatio-Temporal Data presents a systematic approach to key quantitative techniques that incorporate the latest advances in statistical computing as well as hierarchical, particularly Bayesian, statistical modeling, with an emphasis on dynamical spatio-temporal models.

The book is suitable for graduate students, professional statisticians, and researchers and practicioners in the field of applied mathematics, engineering, and the environmental and health sciences.

Further details are available at .