Publications by members of NIASRA are available online via UOW Research Online and UOW Scholars.
The National Institute for Applied Statistics Research Australia (NIASRA) Working Papers Series aims to publish high-quality original research, based on work in progress.
These papers are free for private and educational use; they may not be reproduced or amended. Please contact NIASRA for permission to quote. The papers are copyrighted by the authors. All papers are in PDF format.
| Working Paper Number | Author/s | Title |
|---|---|---|
| 01-25 | Josh Jacobson, Michael Bertolacci, Andrew Zammit-Mangion, Andrew Schuh, and Noel Cressie |
WOMBAT v2.S: A Bayesian Inversion Framework for Attributing Global CO2 Flux Components (PDF 3464kb) |
| 02-25 | Bao Anh Vu, David Gunawan, and Andrew Zammit-Mangion | Recursive Variational Gaussian Approximation with the Whittle Likelihood for Linear Non-Gaussian State Space Models (PDF 4642kb) |
| 03-25 | Tin Lok James Ng, Kwok-Kun Kwong, Jiakun Liu, and Andrew Zammit-Mangion |
Bayesian Sphere-On-Sphere Regression with Optimal Transport Maps (PDF 4929kb) |
| 04-25 | Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie, and Raphaël Huser |
Neural Parameter Estimation with Incomplete Data (PDF 11070kb) |
| Working Paper Number | Author/s | Title |
|---|---|---|
| 01-20 | Bohai Zhang, Noel Cressie | Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice |
| 02-20 | Noel Cressie, Thomas Suesse | Great Expectations and Even Greater Exceedances from Spatially Referenced Data |
| 03-20 | Noel Cressie | When is it Data Science and When is it Data Engineering? |
| 04-20 | John Rayner | Adjustments to the Kruskal-Wallis, Friedman and Durbin Tests When Ties Occur and Mid-ranks are Used |
| 05-20 | Andrew Zammit-Mangion | Discussion on "A high-resolution bilevel skew-t stochastic generator for assessing Saudi Arabia's wind energy resources" |
| 06-20 | Noel Cressie, Christopher K. Wikle | Measuring, Mapping, and Uncertainty Quantification in the Space-Time Cube |
| 07-20 | Harsh Raman, Brett McVittie, Ramethaa Pirathiban, Rosy Raman, Yuanyuan Zhang, Denise M. Barbulescu, Yu Qiu, Shengyi Liu, and Brian Cullis | Genome-Wide Association Mapping Identifies Novel Loci for Quantitative Resistance to Blackleg Disease in Canola |
| Working Paper Number | Author/s | Title |
|---|---|---|
| 01-15 | Noel Cressie and Emily L. Kang | Hot Enough for You? A Spatial Exploratory and Inferential Analysis of North American Climate-Change Projections |
| 02-15 | Noel Cressie, Sandy Burden, Walter Davis, Pavel Krivitsky, Payam Mokhtarian, Thomas Suesse, and Andrew Zammit-Mangion | Capturing Multivariate Spatial Dependence: Model, Estimate, and then Predict |
| 03-15 | James O. Chipperfield, Margo L. Barr, and David G. Steel | Split Questionnaire Designs: are they an efficient design choice? |
| 04-15 | T. Suesse, J.C.W Rayner, and O. Thas | Smooth Tests of Fit for Finite Mixture Distributions |
| 05-15 | Pavel N. Krivitsky and Martina Morris |
Inference for Social Network Models from Egocentrically-Sampled Data, with Application to Understanding Persistent Racial Disparities in HIV Prevalence in the US. NOTE: This working paper has been superseded. The final version has been published at Krivitsky, P. N. & Morris, M. Inference for Social Network Models from Egocentrically-Sampled Data, with Application to Understanding Persistent Racial Disparities in HI Prevalence in the US. Annals of Applied Statistics, 2017, 11, 427-455. doi:10.1214/16-AOAS1010 |
| 06-15 | D.J. Best and J.C.W Rayner | A Note on the CMH General Association Statistic and Square Contingency Tables |
| 07-15 | Noel Cressie and Andrew Zammit-Mangion | Multivariate Spatial Covariance Models: A Conditional Approach |
| 08-15 | Noel Cressie and Raymond L. Chambers | Comment: Spatial sampling designs depend as much on “how much?” and “why?” as on “where?” |
| 09-15 | Sandy Burden, Noel Cressie, and David Steel | The SAR model for very large datasets: A reduced rank approach |
| 10-15 | Thomas Suesse, Mohammad-Reza Namazi-Rad, Payam Mokhtarian, and Johan Barthelemy | Estimating Cross-Classified Population Counts of Multidimensional Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts |
| 11-15 | Pavel N. Krivitsky |
Using Contrastive Divergence to Seed Monte Carlo MLE for Exponential-Family Random Graph Models NOTE: This working paper has been superseded. The final version has been published at Using Contrastive Divergence to Seed Monte Carlo MLE for Exponential-Family Random Graph Models. Computational Statistics & Data Analysis, 2017, 107, 149-161. doi:10.1016/j.csda.2016.10.015 |
| 12-15 | Noel Cressie and Sandy Burden | Figures of Merit for Simultaneous Inference and Comparisons in Simulation Experiments |
| 13-15 | Anoop Chaturvedi, Ashutosh Kumar Dubey, and Chandra Gulati | Statistical Process Control for Autocorrelated Data on Grid |
| 14-15 | Andrew Zammit-Mangion, Noel Cressie, Anita L. Ganesan, Simon O' Doherty, and Alistair J. Manning | Spatio-Temporal Bivariate Statistical Models for Atmospheric Trace-gas Inversion |
| 15-15 | Robert Graham Clark | Efficiency and Robustness in Distance Sampling |
| 16-15 | D.J. Best and J.C.W. Rayner | A note on a moment test of fit for a mixture of two poisson distributions |
| 17-15 | Luke Muzur, Thomas Suesse, and Pavel N. Krivitsky | Investigating Foreign Portfolio Investment Holding: Gravity Model with Social Network Analysis |
| Working Paper Number | Author/s | Title |
|---|---|---|
| 01-08 | Carole Birrell, David G. Steel and Yan-Xia Lin | Seasonal Adjustment of Aggregated Series using Univariate and Multivariate Basic Structural Models UPDATED VERSION IN 2010 SERIES NO. 01-10 |
| 02-08 | James O. Chipperfield and David G. Steel | Design and Estimation for Split Questionnaire Surveys |
| 03-08 | Ray Chambers and Hukum Chandra | Improved Direct Estimators for Small Areas |
| 04-08 | Nicholas von Sanden and David Steel | Optimal Estimation of Interviewer Effects for Binary Response Variables through Partial Interpenetration |
| 05-08 | Robert G. Clark and Raymond L. Chambers | Adaptive Calibration for Prediction of Finite Population Totals |
| 06-08 | Ray Chambers and Hukum Chandra | Multipurpose Small Area Estimation |
| 07-08 | Ray Chambers and Hukum Chandra | Estimation of small domain means for zero contaminated skewed data |
| 08-08 | Ray Chambers | Measurement Error in Auxiliary Information |
| 09-08 | Ray Chambers, Hukum Chandra and Nikos Tzavidis | On Robust Mean Squared Error Estimation for Linear Predictors for Domains UPDATED VERSION IN 2009 SERIES NO.11-09 |
| 10-08 | Hukum Chandra and Ray Chambers | Small Area Estimation Under Transformation To Linearity |
| 11-08 | David Steel and Craig McLaren | Design and Analysis of Repeated Surveys |
| 12-08 | Ray Chambers and Suojin Wang | Maximum Likelihood Logistic Regression With Auxiliary Information |
| 13-08 | Nikos Tzavidis and Ray Chambers | Robust Prediction of Small Area Means and Distributions UPDATED VERSION IN 2009 SERIES NO. 10-09 |
| 14-08 | Nicola Salvati, Nikos Tzavidis, Monica Pratesi and Ray Chambers | Small Area Estimation Via M-quantile Geographically Weighted Regression UPDATED VERSION IN 2009 SERIES NO.09-09 |
| 15-08 | Nicola Salvati, Monica Pratesi, Nikos Tzavidis and Ray Chambers | Spatial M-quantile Models for Small Area Estimation |
| 16-08 | Robert G. Clark and Tanya Strevens | Design and Analysis of Clustered, Unmatched Resource Selection Studies |
| 17-08 | D. Ruppert, Matt P Wand and Raymond J. Carroll | Semiparametric Regression During 2003-2007 |
| 18-08 | Matt P. Wand | Semiparametric Regression and Graphical Models |
| 19-08 | Tarn Duong, Inge Koch and Matt P. Wand | Highest density difference region estimation with application to flow cytometric data |
| 20-08 | Y. Fan, D.S. Leslie and Matt P. Wand | Generalised linear mixed model analysis via sequential Monte Carlo sampling |
| 21-08 | G. Kauermann, John T Ormerod and Matt P. Wand | Parsimonious classification via generalised linear mixed models |
| 22-08 | J. Staudenmayer and E. E. Lake and Matt P. Wand | Robustness for general design mixed models using the t-distribution |
| 23-08 | Robert G. Clark | Sampling for Subpopulations in Two-Stage Surveys |
The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these “big data” 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:
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 https://spacetimewithr.org. 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’ 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 Wollongong, 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 Wollongong, 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 ‘interval’ or ‘ratio’ 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.
“Design of Experiments for Generalized Linear Models” has been published by CRC Press in its Chapman & Hall/ CRC Press Interdisciplinary Statistics. For more details see CRC Press.
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’ 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 CRC Press.
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 CRC Press.
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.
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 Wiley Press.