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National Institute for Applied Statistics Research Australia

Publications & working papers

Publications

Publications by members of NIASRA are available online via UOW Research Online and UOW Scholars. 

Working Papers

Statistics Working Paper Series 2026

Working Paper Number Author/s Title
01-26 Noel Cressie and Yi Cao
02-26 Bao Anh Vu, Andrew Zammit-Mangion, David Gunawan, Felicity S. McCormack, and Noel Cressie
 

Statistics Working Paper Series 2025

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)
05-25 Anjana Wijayawardhana, David Gunawan, and Thomas Suesse Non-Gaussian Simultaneous Autoregressive Models with Missing Data (PDF 2337kb)
06-25 David Gunawan, David Nott, and Robert Kohn Fast Variational Boosting for Latent Variable Models (PDF 1889kb)
07-25 Alan R. Pearse, David Gunawan, and Noel Cressie Bayesian Copula-Based Spatial Random Effects Models for Inference with Complex Spatial Data (PDF 16573kb)
08-25 Brian Cullis, Alison Smith, David Hughes, and David Butler  
 

Statistics Working Paper Series 2024

Working Paper Number Author/s Title
01-24 Eric J. Beh Looking Into the i of the Storm: A Study of the Mid-1880’s Indices of Finley, Gilbert, Peirce and Doolittle and their Place in Contingency Table Analysis (PDF 1015kb)
02-24 Dan Pagendam, Noel Cressie, Jeff Baldock, David Clifford, Ryan Farquharson, Lawrence Murray, Mike Beare, and Denis Curtin CQUESST: A Dynamical Stochastic Framework for Predicting Soil-Carbon Sequestration (PDF 4148kb)
03-24 Eric J. Beh and Rosaria Lombardo A General Similarity Measure for Simple Correspondence Analysis (PDF 381kb)
04-24 Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran, Maurizio
Filippone, and Noel Cressie		 Spatial Bayesian Neural Networks (PDF 6992kb)
05-24 David Gunawan, William Griffiths, and Duangkamon Chotikapanich Bayesian Inference for Multidimensional Welfare Comparisons (PDF 1014kb)
06-24 Xiaotian Zheng, Noel Cressie, David A. Clarke, Melodie A. McGeoch, and Andrew Zammit-Mangion Spatial-Statistical Downscaling with Uncertainty Quantification in Biodiversity Modelling (PDF 2931kb)
 

Statistics Working Paper Series 2023

Working Paper Number Author/s Title
01-23 Luke Mazur and Robin Thompson Computational Implementation of Supernodal Variants for Cholesky Factorization and Calculation of the SIS (PDF 446kb)
02-23 Alison Smith and Brian Cullis Inference for Pairwise Comparisons of Random Variety Effects (PDF 266kb)
03-23 Tin Lok James Ng and Andrew Zammit-Mangion Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport (PDF 1120kb)
04-23 Matthew Sainsbury-Dale, Jordan Richards, Andrew Zammit-Mangion, and Raphaël Huser Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks (PDF 8538kb)
05-23 Josh Jacobson, Noel Cressie, and Andrew Zammit-Mangion Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data (PDF 4366kb)
06-23 Tin Lok James Ng and Andrew Zammit-Mangion Mixture Modeling with Normalizing Flows for Spherical Density Estimation (PDF 3244kb)
07-23 Christopher K. Wikle and Andrew Zammit-Mangion Statistical Deep Learning for Spatial and Spatio-Temporal Data (PDF 1961kb)
08-23 Matthew Sainsbury-Dale, Andrew Zammit-Mangion, and Raphaël Huser Likelihood-Free Parameter Estimation with Neural Bayes Estimators (PDF 4171kb)
09-23 Laura Cartwright, Andrew Zammit-Mangion, and Nicholas Deutscher Emulation of Greenhouse-Gas Sensitivities using Variational Autoencoders (PDF 3314kb)
10-23 Matthew Sainsbury-Dale, Andrew Zammit-Mangion, and Noel Cressie Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK (PDF 7875kb)
11-23 Quan Vu, Matthew T. Moores, and Andrew Zammit-Mangion Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants (PDF 1740kb)
12-23 Quan Vu, Andrew Zammit-Mangion, and Stephen J. Chuter Constructing Large Nonstationary Spatio-Temporal Covariance Models via Compositional Warpings (PDF 5975kb)
13-23 Noel Cressie Adapting Statistical Science for a Fast-Changing Climate (PDF 523kb)
14-23 Noel Cressie, Andrew Zammit-Mangion, Josh Jacobson, and Michael Bertolacci Earth’s CO2 Battle: A View from Space (PDF 1174kb)
15-23 Noel Cressie Robodebt not only Broke the Laws of the Land – it also Broke Laws of Mathematics (PDF 238kb)
16-23 Dennis Trewin, Nicholas Fisher, and Noel Creesie The Robodebt Tragedy (PDF 267kb)
17-23 Michael Bertolacci, Andrew Zammit-Mangion, Andrew Schuh, Beata Bukosa, Jenny Fisher, Yi Cao, Aleya Kaushik, and Noel Cressie Inferring Changes to the Global Carbon Cycle with WOMBAT v2.0, a Hierarchical Fux-Inversion Framework (PDF 3162kb)
18-23 Victoria L. Leaver, Robert G. Clark, Pavel N. Krivitsky, and Carole L. Birrell A Comparison of Likelihood-Based Methods for Size-Biased Sampling (PDF 306kb) 
19-23 David Gunawan, William Griffiths, and Duangkamon Chotikapanich Inequality in Education: A Comparison of Australian Indigenous and Nonindigenous Populations (PDF 446kb)
20-23 Viet-Hung Dao, David Gunawan, Robert Kohn, Minh-Ngoc Tran, Guy E. Hawkins, and Scott D. Brown Bayesian Inference for Evidence Accumulation Models with Regressors (PDF 5154kb)
21-23 Ting-Wu Wang, Eric J. Beh, Rosaria Lombardo, and Ian W. Renner Profile Transformations for Reciprocal Averaging and Singular Value Decomposition (522kb)

 

 

 

 

Statistics Working Paper Series 2022

Working Paper Number Author/s Title
01-22 Giri Gopalan, Andrew Zammit-Mangion, and Felicity McCormack
02-22 Tin Lok James Ng and Andrew Zammit-Mangion
03-22 Andrew Zammit-Mangion, Michael Bertolacci, Jenny Fisher, Ann Stavert, Matthew L. Rigby, Yi Cao, and Noel Cressie
04-22 Noel Cressie, Matthew Sainsbury-Dale, and Andrew Zammit-Mangion
05-22 John. C. W. Rayner and Glen C. Livingston Jr
06-22 Eric J. Beh and Rosaria Lombardo
07-22 Noel Cressie
08-22 Noel Cressie
09-22 David Butler and Brian Cullis On Model Based Design of Comparative Experiments in R (PDF 1145kb)

 

 

 

 

Statistics Working Paper Series 2021

Working Paper Number Author/s Title
01-21 Noel Cressie
02-21 Noel Cressie and Matthew T. Moores
03-21 Andrew Zammit-Mangion and Noel Cressie
04-21 Quan Vu, Andrew Zammit-Mangion, and Noel Cressie
05-21 Alison Smith, Adam Norman, Haydn Kuchel, and Brian Cullis
06-21 Noel Cressie and Christopher K. Wikle
07-21 Alison Smith, Aanandini Ganesalingam, Christopher Lisle, Gururaj Kadkol, Kristy Hobson, and Brian Cullis
08-21 David Gunawan, Robert Kohn, and David Nott
09-21 David Gunawan, Robert Kohn, and David Nott
10-21 David Gunawan, Robert Kohn, and Minh Ngoc Tran
11-21 Harsh Raman, Rosy Raman, Ramethaa Pirathiban, Brett McVittie, Niharika Sharma, Shengyi Liu, Yu Qiu, Anyu Zhu, Andrzej Killian, Brian Cullis, Graham D. Farquhar, Hilary S. Williams, Rosemary White, David Tabah, Andrew Easton, and Yuanyuan Zhang
12-21 David Gunawan, William Griffiths, and Duangkamon Chotikapanich
13-21 David Gunawan, William Griffiths, and Duangkamon Chotikapanich
14-21 Quan Vu, Yi Cao, Josh Jacobson, Alan R. Pearse, and Andrew Zammit-Mangion
15-21 Ivy Liu, Thomas Suesse, Daniel Fernandez, Samuel Harvey, and John Randal
16-21 J.C.W. Rayner, P. Rippon, T. Suesse, and O. Thas
17-21 Gururaj Kadkol, Jess Meza, Steven Simpfendorfer, Steve Harden, and Brian Cullis Genetic Variance for Fusarium Crown Rot Tolerance in Durum Wheat (PDF 1025kb)
18-21 Alison B. Smith and Brian R. Cullis
19-21 Diane Hindmarsh and David Steel
20-21 Chris Lisle, Alison Smith, Carole L. Birrell, and Brian Cullis
21-21 Ray Chambers, Stephen Beare, Scott Peak, and Mohammed Al-Kalbani
22-21 Ray Chambers, Setareh Ranjbar, Nicola Salvati, and Barbara Pacini

 

 

 

Statistics Working Paper Series 2020

Working Paper NumberAuthor/sTitle
01-20 Bohai Zhang, Noel Cressie
02-20 Noel Cressie, Thomas Suesse
03-20 Noel Cressie
04-20 John Rayner
05-20 Andrew Zammit-Mangion
06-20 Noel Cressie, Christopher K. Wikle
07-20 Harsh Raman, Brett McVittie, Ramethaa Pirathiban, Rosy Raman, Yuanyuan Zhang, Denise M. Barbulescu, Yu Qiu, Shengyi Liu, and Brian Cullis

Statistics Working Paper Series 2019

Working paper number Author/s Working paper title
01-19 John Brakenbury, P.Y O’Shaughnessy, Yan-Xia Lin  (pdf)
02-19 D.J. Best, J.C.W. Rayner  (pdf)
03-19 Hai Nguyen, Noel Cressie, and Jonathan Hobbs  (pdf)
04-19 Thomas Suesse and Andrew Zammit-Mangion  (pdf)
05-19 Hsin-Cheng Huang, Noel Cressie, Andrew Zammit-Mangion, and Guowen Huang  (pdf)
06-19 Andrew Zammit-Mangion, Tin Lok James Ng, Quan Vu and Maurizio Filippone  (pdf)
07-19 Laura Cartwright, Andrew Zammit-Mangion, Sangeeta Bhatia, Ivan Schroder, Frances Phillips, Trevor Coates, Karita Neghandhi, Travis Naylor, Martin Kennedy, Steve Zegelin, Nick Wokker, Nicholas M. Deutscher, and Andrew Feitz  (pdf)
08-19 Pavel N. Krivitsky, Martina Morris, and Micha艂 Bojanowski  
09-19 Andrew Zammit-Mangion and Jonathan Rougier
10-19 Brian Cullis and Alison Smith
11-19 Bradley Wakefield, Yan-Xia Lin, Rathin Sarathy, Krishnamurty Muralidhar
12-19 Andrew Zammit-Mangion and Christopher K. Wikle
13-19 Brian R. Cullis, Alison B. Smith, Nicole A. Cocks and David G. Butler

Statistics Working Paper Series 2018

Working Paper NumberAuthor/sTitle
01-18 Andrew Zammit-Mangion, Noel Cressie, and Clint Shumack
02-18 Andrew Zammit-Mangion and Jonathan Rougier
03-18 D.J. Best and J.C.W. Rayner
04-18 David G Butler and Brian R Cullis
05-18 Alison Smith and Brian Cullis
06-18 Alison B. Smith and Brian R. Cullis
07-18 David Hughes
Supervised by Professor Brian Cullis and Lauren Borg
08-18 Nicole. A. Cocks, Timothy. J. March, Thomas. B. Biddulph, Alison. B. Smith, and Brian. R. Cullis
09-18 David Butler, Brian Cullis, Arthur Gilmour and Robin Thompson
10-18 Carole L. Birrell, David G. Steel, Marijka J. Batterham and Ankur Arya
11-18 Kevin W. Bowman, Noel Cressie, Xin Qu, and Alex Hall
12-18 D.J. Best and J.C.W. Rayner
13-18 Noel Cressie and C茅cile Hardouin
14-18 Brian Cullis, Nicole Cocks, Alison Smith, and David Butler
15-18 Brian Cullis, Alison Smith, Ari Verbyla, Robin Thompson, and Sue Welham

Statistics Working Paper Series 2017

Working Paper Number Author/s Title
01-17 Noel Cressie
02-17 Bohai Zhang, Noel Cressie, and Debra Wunch
03-17 Andrew Zammit-Mangion and Noel Cressie
04-17 Thomas Suesse and Andrew Zammit-Mangion
05-17 D.J. Best and J.C.W. Rayner
06-17 J.C.W. Rayner and D.J. Best
07-17 Yue Ma, Yan-Xia Lin and Rathindra Sarathy
08-17 Bohai Zhang and Noel Cressie
09-17 D.J. Best and J.C.W. Rayner
10-17 Yan-Xia Lin
11-17 Brian Cullis, Nicole Cocks, Alison Smith and David Butler

Statistics Working Paper Series 2016

Working Paper NumberAuthor/sTitle
01-16 Mohammad-Reza Namazi-Rad, Robert Tanton, David Steel, Payam Mokhtarian, Sumonkanti Das
02-16 Margo L Barr, Robert Clark and David G Steel
03-16 Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie
04-16 N. Cressie, R. Wang, M. Smyth, and C. E. Miller
05-16 D. J. Best and J. C. W. Rayner
06-16 Andrew Zammit-Mangion, Noel Cressie, and Anita L. Ganesan
07-16 C茅cile Hardouin and Noel Cressie
08-16 Bohai Zhang, Noel Cressie, and Debra Wunch
09-16 Noel Cressie, Sandy Burden, Clint Shumack, Andrew Zammit-Mangion, and Bohai Zhang
10-16 Brian Cullis, Emi Tanaka, Lauren Borg and Alison Smith
11-16 Brian Cullis and Alison Smith
12-16 Alison Smith, Emi Tanaka, Brian Cullis and Robin Thompson
13-16 Hai Nguyen, Noel Cressie, and Amy Braverman
14-16 Georgina Davies and Noel Cressie
15-16 Yuliya Marchetti, Hai Nguyen, Amy Braverman, and Noel Cressie
16-16 Noel Cressie, Rui Wang, and Ben Maloney

Statistics Working Paper Series 2015

Working Paper Number Author/s Title
01-15 Noel Cressie and Emily L. Kang
02-15 Noel Cressie, Sandy Burden, Walter Davis, Pavel Krivitsky, Payam Mokhtarian, Thomas Suesse, and Andrew Zammit-Mangion
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
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
07-15 Noel Cressie and Andrew Zammit-Mangion
08-15 Noel Cressie and Raymond L. Chambers
09-15 Sandy Burden, Noel Cressie, and David Steel
10-15 Thomas Suesse, Mohammad-Reza Namazi-Rad, Payam Mokhtarian, and Johan Barthelemy
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
13-15 Anoop Chaturvedi, Ashutosh Kumar Dubey, and Chandra Gulati
14-15 Andrew Zammit-Mangion, Noel Cressie, Anita L. Ganesan, Simon O' Doherty, and Alistair J. Manning
15-15 Robert Graham Clark
16-15 D.J. Best and J.C.W. Rayner
17-15 Luke Muzur, Thomas Suesse, and Pavel N. Krivitsky

Statistics Working Paper Series 2014

Working Paper Number Author/s Title
01-14 Margo L. Barr, Raymond A. Ferguson, Phil J. Hughes, and David G. Steel
02-14 Hai Nguyen, Matthias Katzfuss, Noel Cressie, and Amy Braverman
03-14 Noel Cressie and Sandy Burden
04-14 D. Clifford, D. Pagendam, J. Baldock, N. Cressie, R. Farquharson, M. Farrell, L. Macdonald, and L. Murray
05-14 T. Stough, A. Braverman, N. Cressie, E. Kang, A.M. Michalak, H. Nguyen, and K. Sahr
06-14 J.C.W. Rayner, D.J. Best, and O. Thas
07-14 D.J. Best and J.C.W. Rayner
08-14 Wilford B. Molefe and Robert Graham Clark
09-14 Lili Zhuang and Noel Cressie
10-14 Heni Puspaningrum, Yan-Xia Lin, and Chandra Gulati
11-14 Yan-Xia Lin
12-14 Stephen Beare
13-14 Thomas Suesse and Ray Chambers
14-14 Thomas Suesse and Ivy Liu
15-14 Yan-Xia Lin and Mark James Fielding
16-14 Jonathan R. Bradley, Noel Cressie, and Tao Shi
17-14 Aritra Sengupta, Noel Cressie, Brian H. Kahn, and Richard Frey
18-14 Robert Graham Clark

Statistics working paper series 2013

Working Paper Number Author/s Title
01-13 Mohammad-Reza Namazi-Rad and David Steel
02-13 Simon Diffey, Alan Welsh, and Alison Smith
03-13 Robert Graham Clark
04-13 Noel Cressie
05-13 Lili Zhuang, Noel Cressie, Laura Pomeroy, and Daniel Janies
06-13 N Cressie, M Morara, B. Buxton, N. McMillan, W. Strauss, and N. Wilson
07-13 Aritra Sengupta and Noel Cressie
08-13 Aaron T. Porter, Scott H. Holan, Christopher K. Wikle, and Noel Cressie
09-13 Jonathan R. Bradley, Noel Cressie, and Tao Shi
10-13 Aritra Sengupta, Noel Cressie, Richard Frey, and Brian H. Kahn
11-13 David G. Steel and Robert Graham Clark
12-13 D.J. Best and J.C.W Rayner
13-13 Ray Chambers, Emanuela Dreassi, and Nicola Salvati
14-13 Nikos Tzavidis, M Giovanna Ranalli, Nicola Salvati, Emanuela Dreassi, and Ray Chambers
15-13 Sandy Burden and David Steel
16-13 Gunky Kim and Raymond Chambers
17-13 Gunky Kim and Raymond Chambers
18-13 David Clifford, Noel Cressie, Jacqueline R. England, Stephen H. Roxburgh, and Keryn I. Paul
19-13 Wilford B. Molefe and Robert Graham Clark
20-13 Sandy Burden and David Steel
21-13 Jonathan R. Bradley, Noel Cressie, and Tao Shi
22-13 Brian Cullis, David Butler, Sue Welham, Alison Smith, Beverley Gogel and Robin Thompson

Statistics Working Paper Series 2012 

Working Paper Number Author/s Title
01-12 Mark Tranmer, David Steel and William J Browne
02-12 David Steel
03-12 Gunky Kim and Raymond Chambers
04-12 David Allingham and John.C.W.Rayner
05-12 John. C.W. Rayner and D.J. Best
06-12 Robert Graham Clark  
07-12 Margo L Barr, Jason J van Ritten, David G Steel and Sarah V Thackway
08-12 Luise P. Lago and Robert G. Clark
09-12  Margo L Barr, Anthony Dillon, Mazen Kassis and David G Steel   
10-12 Robert Clark and Robert Templeton
11-12 Thomas Suesse and Ray Chambers
12-12 Ray Chambers, Nicola Salvati and Nikos Tzavidis
13-12 Paul A. Butcher, Matt K. Broadhurst, Karina C. Hall, Brian R. Cullis, Shane R. Raidal
14-12 Maman Fathurrohman and Anne Porter
15-12 Maman Fathurrohman, Anne Porter and Annette L. Worthy
16-12 Alision Smith, David G. Butler, Colin R. Cavanagh and Brian R. Cullis
17-12 Brian Cullis, Sue Welham, Beverley Gogel, David Butler, and Robin Thompson

Statistics Working Paper Series 2011 

Working Paper Number Author/s Title
01-11 Ray Chambers and Hukum Chandra
02-11 Mohammad-Reza Namazi-Rad and David Steel

What Level of Statistical Model Should We Use in Small Domain Estimation? UPDATED VERSION IN 2013 SERIES NO. 01-13
03-11 Alison Smith, Brian Cullis and Matthew Nelson
04-11 Thomas Suesse and Ivy Liu
05-11 Jinda Kongcharoen, Yan-Xia Lin, Rachael Caldwell, Yiren Yang and Ren Zhang
06-11 Maman Fathurrohman and Anne Porter
07-11 Bothaina Bukhatowa, Anne Porter and Mark Nelson
08-11 Bothaina Bukhatowa, Anne Porter and Mark Nelson
09-11 Ray Chambers
10-11 Gunky Kim and Ray Chambers
11-11 John Best and John Rayner
12-11 David Allingham and John Rayner
13-11 Chaiwat Kosapattarapim, Yan-Xia Lin and Michael McCrae
14-11 Yan-Xia Lin
15-11 Heni Puspaningrum, Yan-Xia Lin and Chandra Gulati
16-11 M.K. Broadhurst, P.A. Butcher, K.C. Hall, B.R. Cullis and S.P. McGrath
17-11 David Butler, Brian Cullis, and Julian Taylor

Statistics Working Paper Series 2010

Working Paper Number Author/s Title
01-10 Carole L. Birrell, David G. Steel and Yan-Xia Lin
02-10 Robert Graham Clark and
Samuel Allingham
03-10 Robert Clark, Paul Milham, Andrew Thomas, John Morrison
 
04-10

Hall, P., Pham, T., Wand, M.P. and

 Wang, S.S.J

05-10 Wand, M.P., Ormerod, J.T., Padoan, S.A. and Fruhwirth, R.
06-10 Wang, S.S.J and Wand, M.P.
07-10 Faes, C., Ormerod, J.T. and Wand, M.P.
08-10 Chacon, J.E.; Duong, T. and Wand, M.P.
09-10 Y.-X. Lin, V. Baladandauthapani, V. Bonato and K.-A. Do
10-10 Y -X. Lin, V. Baladandayuthapani,V. Bonato and K.-A. DO
11-10  David Griffiths, Martin Bunder, Chandra Gulati, Takeo Onizawa
12-10 Carole Birrell, Yan-Xia Lin, David G. Steel
13-10 B.M. Brown, Thomas Suesse and Von Bing Yap
14-10 Thomas Suesse, Ivy Liu
15-10 Brian Cullis, David Butler, Daryl Mares, Kolumbina Mrva and Hai Yunn Law
16-10 James O. Chipperfield and David G. Steel
17-10 Stephen Beare, Ray Chambers, Scott Peak, Jennifer M Ring
18-10 Klairung Samart and Ray Chambers
19-10 Hukum Chandra, Nicola Salvati and U.C. Sud
20-10 Nicola Salvati, Hukum Chandra and Ray Chambers
21-10 Hukum Chandra, Nicola Salvati, Ray Chambers and Nikos Tzavidis
22-10 Gunky Kim and Raymond Chambers
23-10 Thomas Suesse
24-10 Sandy Burden, Yamine Probst, David Steel and Linda Tapsell
25-10 Bothaina Bukhatowa, Anne Porter and Mark Nelson

Statistics Working Paper Series 2009

Working Paper Number  Author/s  Title
01-09 Loai Mohamoud Al-Zou'bi, Robert Graham Clark and David G. Steel
02-09 Samworth, R.J. and Wand, M.P.
03-09 Naumann, U., Luta, G. and Wand, M.P
04-09 Ormerod, J.T. and Wand, M.P.
05-09 Alkadiri, M., Carroll, R.J. and Wand, M.P.
06-09 Hall, P., Ormerod, J.T. and Wand, M.P.
07-09 Ormerod, J.T. and Wand, M.P.
08-09 Chacon, J.E., and Duong, T. and Wand, M.P.
09-09 Nikos Tzavidis, Monica Pratesi, Ray Chambers
10-09 Nikos Tzavidis, Stefano Marchetti and Ray Chambers
11-09 Ray Chambers, Hukum Chandra, Nikos Tzavidis
12-09 Ray Chambers, Nikos Tzavidis, Nicola Salvati
13-09 Dr George Sofronov
14-09 Stephen Beare, Ray Chambers, Scott Peak
15-09 Hukum Chandra, Ray Chambers, Nicola Salvati
16-09 R Chambers, H Chandra, N Salvati and N Tzavidis
17-09 Gunky Kim and Raymond Chambers
18-09 Ray Chambers, James Chipperfield, Walter Davis, Milorad Kovacevic
19-09 Nicola Salvati, Hukum Chandra, M. Giovanna Ranalli and Ray Chambers
20-09 Gandhi Pawitan, David G. Steel
21-09 Heni Puspaningrum, Yan-Xia Lin, Chandra Gulati
22-09 Raed Alzghool, Yan-Xia Lin and Song Xi Chen
23-09 Yan-Xia Lin
24-09 Norhayati Baharun and Anne Porter
25-09 Norhayati Baharun and Anne Porter
26-09 Norhayati Baharun and Anne Porter
27-09 Norhayati Baharun and Anne Porter

 

Statistics Working Paper Series 2008

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  
03-08 Ray Chambers and Hukum Chandra
04-08 Nicholas von Sanden and David Steel
05-08 Robert G. Clark and Raymond L. Chambers
06-08 Ray Chambers and Hukum Chandra  
07-08 Ray Chambers and Hukum Chandra
08-08 Ray Chambers
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
11-08 David Steel and Craig McLaren  
12-08 Ray Chambers and Suojin Wang
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  
16-08  Robert G. Clark and Tanya Strevens
17-08 D. Ruppert, Matt P Wand and Raymond J. Carroll
18-08 Matt P. Wand
19-08 Tarn Duong, Inge Koch and Matt P. Wand
20-08 Y. Fan, D.S. Leslie and Matt P. Wand
21-08 G. Kauermann, John T Ormerod and Matt P. Wand
22-08 J. Staudenmayer and E. E. Lake and Matt P. Wand
23-08 Robert G. Clark

 

Books

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 榴莲视频app 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 榴莲视频app of 榴莲视频app, 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 榴莲视频app of 榴莲视频app, 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 榴莲视频app and Alan Welsh from the Australian National 榴莲视频app 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 .