Publications

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

Statistics Working Paper series

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 Papers

Statistics Working Paper Series 2021

Working Paper NumberAuthor/sTitle
01-21 Noel Cressie A Few Statistical Principles for Data Science (PDF 375kb)
02-21 Noel Cressie and Matthew T. Moores Spatial Statistics (PDF 573kb)
03-21 Andrew Zammit-Mangion and Noel Cressie FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (PDF 5871kb)
04-21 Quan Vu, Andrew Zammit-Mangion, and Noel Cressie Modeling Nonstationary and Asymmetric Multivariate Spatial Covariances via Deformations (PDF 4011kb)
05-21 Alison Smith, Adam Norman, Haydn Kuchel, and Brian Cullis Plant Variety Selection using Interaction Classes Derived from Factor Analytic Linear Mixed Models: Models with Independent Variety Effects (PDF 1869kb)
06-21 Noel Cressie and Christopher K. Wikle Modeling Dependence in Spatio-Temporal Econometrics (PDF 242kb)

 

 

Statistics Working Paper Series 2019

Working paper numberAuthor/sWorking paper title
01-19 John Brakenbury, P.Y O’Shaughnessy, Yan-Xia Lin Protecting the Privacy of Smart Meter Data: the Differential Privacy Approach and the Multiplicative Noise Approach (pdf)
02-19 D.J. Best, J.C.W. Rayner A Marginal Homogeneity Test for Blocked Categorical Data (pdf)
03-19 Hai Nguyen, Noel Cressie, and Jonathan Hobbs Optimal Estimation Retrievals: Implications and Consequences when the Prior's Mean and Covariance are Misspecifed (pdf)
04-19 Thomas Suesse and Andrew Zammit-Mangion Marginal Maximum Likelihood Estimation of Conditional Autoregressive Models with Missing Data (pdf)
05-19 Hsin-Cheng Huang, Noel Cressie, Andrew Zammit-Mangion, and Guowen Huang False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated Data (pdf)
06-19 Andrew Zammit-Mangion, Tin Lok James Ng, Quan Vu and Maurizio Filippone Deep Compositional Spatial Models (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 Bayesian Atmospheric Tomography for Detection and Quantification of Methane Emissions: Application to Data from the 2015 Ginninderra Release Experiment (pdf)
08-19 Pavel N. Krivitsky, Martina Morris, and Michał Bojanowski Inference for Exponential-Family Random Graph Models from Egocentrically-Sampled Data with
Alter–Alter Relations
 
09-19 Andrew Zammit-Mangion and Jonathan Rougier Multi-Scale Process Modelling and Distributed Computation for Spatial Data 
10-19 Brian Cullis and Alison Smith REML to ML 
11-19 Bradley Wakefield, Yan-Xia Lin, Rathin Sarathy, Krishnamurty Muralidhar The Multivariate Moments Problem: A Practical High Dimensional Density Estimation Method 
12-19 Andrew Zammit-Mangion and Christopher K. Wikle Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting 
13-19 Brian R. Cullis, Alison B. Smith, Nicole A. Cocks and David G. Butler The Design of Early Stage Plant Breeding Trials using Genetic Relatedness 

Statistics Working Paper Series 2018

Working Paper NumberAuthor/sTitle
01-18 Andrew Zammit-Mangion, Noel Cressie, and Clint Shumack On Statistical Approaches to Generate Level 3 Products from Remote Sensing Retrievals
02-18 Andrew Zammit-Mangion and Jonathan Rougier A Sparse Linear Algebra Algorithm for Fast Computation of Prediction Variances with Gaussian Markov Random Fields
03-18 D.J. Best and J.C.W. Rayner Quade's test and the analysis of ordinal categorical data
04-18 David G Butler and Brian R Cullis Optimal Design under the Linear Mixed Model
05-18 Alison Smith and Brian Cullis Design Tableau: An Aid to Specifying the Linear Mixed Model for a Comparative Experiment
06-18 Alison B. Smith and Brian R. Cullis Plant Breeding Selection Tools Built on Factor Analytic Mixed Models for Multi-Environment Trial Data
07-18 David Hughes
Supervised by Professor Brian Cullis and Lauren Borg
A Method of QTL Analysis Utilising Spatial Models for Marker Effects
08-18 Nicole. A. Cocks, Timothy. J. March, Thomas. B. Biddulph, Alison. B. Smith, and Brian. R. Cullis A Statistical Framework for the Provision of Grower and Breeder Information on the Frost Susceptibility of Wheat in Australia
09-18 David Butler, Brian Cullis, Arthur Gilmour and Robin Thompson Package ‘asreml’
10-18 Carole L. Birrell, David G. Steel, Marijka J. Batterham and Ankur Arya How to use Replicate Weights in Health Survey Analysis using the National Nutrition and Physical Activity Survey as an Example
11-18 Kevin W. Bowman, Noel Cressie, Xin Qu, and Alex Hall A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow-Albedo Feedback
12-18 D.J. Best and J.C.W. Rayner A Note on Bowker’s Symmetry Statistic, its Components and Bootstrap P-Values
13-18 Noel Cressie and Cécile Hardouin A Diagonally Weighted Matrix Norm between Two Covariance Matrices
14-18 Brian Cullis, Nicole Cocks, Alison Smith, and David Butler Sparse Multi-Environment Trial Designs for Early Stage Selection Experiments in Plant Breeding Programmes
15-18 Brian Cullis, Alison Smith, Ari Verbyla, Robin Thompson, and Sue Welham Mixed Models for Data Analysts

Statistics Working Paper Series 2016

Working Paper NumberAuthor/sTitle
01-16 Mohammad-Reza Namazi-Rad, Robert Tanton, David Steel, Payam Mokhtarian, Sumonkanti Das An Unconstrained Statistical Matching Algorithm for Combing Individual and Household Level Geo-Specific Census and Survey Data
02-16 Margo L Barr, Robert Clark and David G Steel Examining associations in cross-sectional studies
03-16 Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie Probabilistic Evaluation of Competing Climate Models
04-16 N. Cressie, R. Wang, M. Smyth, and C. E. Miller Statistical Bias and Variance for the Regularized-inverse Problem: Application to Space-based Atmospheric CO2 Retrievals
05-16 D. J. Best and J. C. W. Rayner Applications of Madansky’s Q
06-16 Andrew Zammit-Mangion, Noel Cressie, and Anita L. Ganesan Non-Gaussian Bivariate Modelling with Application to Atmospheric Trace-gas Inversion
07-16 Cécile Hardouin and Noel Cressie Two-Scale Spatial Models for Binary Data
08-16 Bohai Zhang, Noel Cressie, and Debra Wunch Statistical Properties of Atmospheric Greenhouse Gas Measurements: Looking Down from Space and Looking Up from the Ground
09-16 Noel Cressie, Sandy Burden, Clint Shumack, Andrew Zammit-Mangion, and Bohai Zhang Environmental Informatics
10-16 Brian Cullis, Emi Tanaka, Lauren Borg and Alison Smith Linkage Map Construction for the Cranbrook x Halberd Mapping Population
11-16 Brian Cullis and Alison Smith The Analysis of QTL and QTL x Treatment Experiments using Spatial Models for Marker Effects
12-16 Alison Smith, Emi Tanaka, Brian Cullis and Robin Thompson Linear Mixed Models for Genomic Selection
13-16 Hai Nguyen, Noel Cressie, and Amy Braverman Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets
14-16 Georgina Davies and Noel Cressie Analysis of Variability of Tropical Pacific Sea Surface Temperatures
15-16 Yuliya Marchetti, Hai Nguyen, Amy Braverman, and Noel Cressie Spatial Data Compression via Adaptive Dispersion Clustering
16-16 Noel Cressie, Rui Wang, and Ben Maloney The Atmospheric Infrared Sounder (AIRS) Retrieval, Revisited

Statistics Working Paper Series 2015

Working Paper NumberAuthor/sTitle
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, 201711, 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, 2017107, 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

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 Inclusion of mobile telephone numbers into an ongoing population health survey in New South Wales, Australia using an overlapping dual-frame design: final weighting strategy
02-14 Hai Nguyen, Matthias Katzfuss, Noel Cressie, and Amy Braverman Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets
03-14 Noel Cressie and Sandy Burden Evaluation of Diagnostics for Hierarchical Spatial Statistical Models
04-14 D. Clifford, D. Pagendam, J. Baldock, N. Cressie, R. Farquharson, M. Farrell, L. Macdonald, and L. Murray Re-thinking soil carbon modelling: A stochastic approach to quantify uncertainties
05-14 T. Stough, A. Braverman, N. Cressie, E. Kang, A.M. Michalak, H. Nguyen, and K. Sahr Visualizing massive spatial datasets using multi-resolution global grids
06-14 J.C.W. Rayner, D.J. Best, and O. Thas Extended analysis of at least partially ordered mulit-factor Anova
07-14 D.J. Best and J.C.W. Rayner Smooth tests of fit for more flexible alternatives to the exponential and Poisson: the Lindley and Poisson-Lindley distributions
08-14 Wilford B. Molefe and Robert Graham Clark Model-Assisted Optimal Allocation For Planned Domains Using Composite Estimation
09-14 Lili Zhuang and Noel Cressie Bayesian Hierarchical Statistical SIRS Models
10-14 Heni Puspaningrum, Yan-Xia Lin, and Chandra Gulati Cointegration with a Time Trend and Pairs Trading Strategy: Empirical Study on the S&P 500 Future and Spot Index Prices
11-14 Yan-Xia Lin Density Approximant Based on Noise Multiplied Data
12-14 Stephen Beare Passing the Repeal of the Carbon Tax Back to Wholesale Electricity Prices
13-14 Thomas Suesse and Ray Chambers Using Social Network Information for Survey Estimation
14-14 Thomas Suesse and Ivy Liu Mantel-Haenszel Estimators of a Common Local Odds Ratio for Multiple Response Data
15-14 Yan-Xia Lin and Mark James Fielding Density Approximant Based on Noise multiplied Data: MarkDensity 10.R and its Applications
16-14 Jonathan R. Bradley, Noel Cressie, and Tao Shi A Comparison of Spatial Predictors when Datasets Could be Very Large
17-14 Aritra Sengupta, Noel Cressie, Brian H. Kahn, and Richard Frey Predictive Inference for Big, Spatial, Non-Gaussian Data: MODIS Cloud Data and its Change-of-Support
18-14 Robert Graham Clark Model-Assisted Sample Design of a First Phase Survey with Two Second-Phase Surveys

Statistics working paper series 2013

Working Paper Number Author/s Title
01-13 Mohammad-Reza Namazi-Rad and David Steel What Level of Statistical Model Should We Use in Small Domain Estimation?
02-13 Simon Diffey, Alan Welsh, and Alison Smith A faster and computational more efficent REML (PX)EM algorithm for linear mixed models
03-13 Robert Graham Clark Incorporating Household Type in Mixed Logistic Models for People in Households
04-13 Noel Cressie Environmental Informatics: Uncertainty Quantification in the Environmental Sciences
05-13 Lili Zhuang, Noel Cressie, Laura Pomeroy, and Daniel Janies Multi-species SIR Models for a Dynamical Bayesian Perspective
06-13 N Cressie, M Morara, B. Buxton, N. McMillan, W. Strauss, and N. Wilson A Bayesian multivariate analysis of children's exposure to pesticides
07-13 Aritra Sengupta and Noel Cressie Hierarchical Statistical Modeling of Big Spatial Datasets Using the Exponential Family of Distributions
08-13 Aaron T. Porter, Scott H. Holan, Christopher K. Wikle, and Noel Cressie Spatial Fay-Herriot Models for Small Area Estimation with Functional Covariates
09-13 Jonathan R. Bradley, Noel Cressie, and Tao Shi Local Spatial-Predictor Selection
10-13 Aritra Sengupta, Noel Cressie, Richard Frey, and Brian H. Kahn Statistical Modeling of MODIS Cloud Data Using the Spatial Random Effects Model
11-13 David G. Steel and Robert Graham Clark Potential Gains From Using Unit Level Cost Information In A Model-Assisted Framework
12-13 D.J. Best and J.C.W Rayner Bootstrap P-Values for Cochran's Q, Stuart and Bowker Tests
13-13 Ray Chambers, Emanuela Dreassi, and Nicola Salvati Disease Mapping via Negative Binomial M-quantile Regression
14-13 Nikos Tzavidis, M Giovanna Ranalli, Nicola Salvati, Emanuela Dreassi, and Ray Chambers Poisson M-quantile Regression for Small Area Estimation
15-13 Sandy Burden and David Steel Empirical Zoning Distributions for Small Area Health Data
16-13 Gunky Kim and Raymond Chambers Bias Reduction for Correlated Linkage Error
17-13 Gunky Kim and Raymond Chambers Maximum Likelihood Logistic Regression with Auxiliary Information for Probabilistically Linked Data
18-13 David Clifford, Noel Cressie, Jacqueline R. England, Stephen H. Roxburgh, and Keryn I. Paul Correction factors for unbiased, efficient estimation and prediction of biomass from log-log allometric models
19-13 Wilford B. Molefe and Robert Graham Clark Model-Assisted Optimal Allocation for Planned  Domains Using Composite Estimation
20-13 Sandy Burden and David Steel Constraint Choice for Spatial Microsimulation
21-13 Jonathan R. Bradley, Noel Cressie, and Tao Shi Comparing and Selecting Spatial Predictors Using Local Criteria
22-13 Brian Cullis, David Butler, Sue Welham, Alison Smith, Beverley Gogel and Robin Thompson The Analysis of Tree Breeding Data using ASReml-R

Statistics Working Paper Series 2012 

Working Paper Number Author/s Title
01-12 Mark Tranmer, David Steel and William J Browne Multiple Membership Models for Social Networks and Group Dependencies
02-12 David Steel Potential Gains from Sample Design Using Unit Level Cost Information
03-12 Gunky Kim and Raymond Chambers Unbiased Regression Estimation for Multi-Linked Data in the Presence of Correlated Linkage Error
04-12 David Allingham and John.C.W.Rayner Testing Equality of Variances for Multiple Univariate Normal Populations
05-12 John. C.W. Rayner and D.J. Best Continuous Analogues of Cochran-Mantel-Haenszel Statistics
06-12 Robert Graham Clark  Statistical Learning In Sample Design
07-12 Margo L Barr, Jason J van Ritten, David G Steel and Sarah V Thackway Inclusion of mobile phone numbers into an ongoing population health survey in Australia using an overlapping dual frame design: description of methods, call outcomes and acceptance by staff and respondents
08-12 Luise P. Lago and Robert G. Clark Imputtaion of Household Survey Data using Linear Mixed Models
09-12  Margo L Barr, Anthony Dillon, Mazen Kassis and David G Steel  Can telephone surveys for the whole population provide reliable information on the health of Aboriginal and Torres Strait Islander Australians? 
10-12 Robert Clark and Robert Templeton Sampling the Maori Population using Proxy Screening, the Electoral Roll and Disproportionate Sampling in the New Zealand Health Survey
11-12 Thomas Suesse and Ray Chambers Using Social Network Information for Survey Estimation
12-12 Ray Chambers, Nicola Salvati and Nikos Tzavidis M-Quantile Regression for Binary Data with Application to Small Area Estimation
13-12 Paul A. Butcher, Matt K. Broadhurst, Karina C. Hall, Brian R. Cullis, Shane R. Raidal Assessing barotrauma among angled snapper (Pagrus auratus) and the utility of release methods
14-12 Maman Fathurrohman and Anne Porter Addressing the Needs of a Developing Nation: Electronic Maps of Mathematical Learning Resources Accessible Via the Internet
15-12 Maman Fathurrohman, Anne Porter and Annette L. Worthy Learning Design Map (LDMap) for Mathematics Teachers in Developing Countries and the Benefit of Its Use for Curriculum Review
16-12 Alision Smith, David G. Butler, Colin R. Cavanagh and Brian R. Cullis Multi-phase variety trials using both composite and individual replicate samples: A model-based design approach
17-12 Brian Cullis, Sue Welham, Beverley Gogel, David Butler, and Robin Thompson Modern Applications of Linear Mixed Models in Case Studies: Course Notes

Statistics Working Paper Series 2011 

Working Paper Number Author/s Title
01-11 Ray Chambers and Hukum Chandra A Semiparameric Block Bootstrap for Clustered Data
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 Detecting QTL for photoperiod sensitivity in a Brassica napus doubled haploid population using a linear mixed model with correlated marker effects
04-11 Thomas Suesse and Ivy Liu Modelling Strategies for Repeated Multiple Response Data
05-11 Jinda Kongcharoen, Yan-Xia Lin, Rachael Caldwell, Yiren Yang and Ren Zhang The Analysis of Pattern Change in Intron Sequences
06-11 Maman Fathurrohman and Anne Porter Modifiable and Shareable Electronic Maps of Mathematical Learning Resources for Use in Developing Countries: A Case Study of Bojonegara Sub District, Indonesia
07-11 Bothaina Bukhatowa, Anne Porter and Mark Nelson Exploring learning design in tertiary mathematics
08-11 Bothaina Bukhatowa, Anne Porter and Mark Nelson Experience with Change Evaluations suggests the need for better learning designs: one possibility for mathematics
09-11 Ray Chambers Which Sample Survey Strategy? A Review of Three Different Approaches
10-11 Gunky Kim and Ray Chambers Regression Analysis under Probabilistic Multi-Linkage
11-11 John Best and John Rayner Nonparametric Test for Latin Squares
12-11 David Allingham and John Rayner A Nonparametric Two-Sample Wald Test of Equality of Variances
13-11 Chaiwat Kosapattarapim, Yan-Xia Lin and Michael McCrae Evaluating the Volatility Forecasting Performance of Best Fitting GARCH Models in Emerging Asian Stock Markets
14-11 Yan-Xia Lin The Algorithm of Equal Acceptance Region for Detecting Copy Number Alterations: Applications to Next-Generation Sequencing Data
15-11 Heni Puspaningrum, Yan-Xia Lin and Chandra Gulati Unit Root Tests for ESTAR Models
16-11 M.K. Broadhurst, P.A. Butcher, K.C. Hall, B.R. Cullis and S.P. McGrath Resilience of inshore, juvenile snapper Pagrus auratus to angling and release
17-11 David Butler, Brian Cullis, and Julian Taylor Extensions in Linear Mixed Models and Design of Experiments

Statistics Working Paper Series 2010

Working Paper Number Author/s Title
01-10 Carole L. Birrell, David G. Steel and Yan-Xia Lin Seasonal Adjustment of an Aggregate Series using Univariate and Multivariate Basic Structural Models
02-10 Robert Graham Clark and
Samuel Allingham
Further Simulation Results on Resampling Confidence Intervals for Empirical Varigrams
03-10 Robert Clark, Paul Milham, Andrew Thomas, John Morrison Multinomial Probabilities for Orthophosphate Isotopomers
 
04-10

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

 Wang, S.S.J

Asymptotic Normality and Valid Inference for Gaussian Variational Approximation

05-10 Wand, M.P., Ormerod, J.T., Padoan, S.A. and Fruhwirth, R. Variational Bayes for Elaborate Distributions
06-10 Wang, S.S.J and Wand, M.P. Using Infer.NET for Statistical Analyses
07-10 Faes, C., Ormerod, J.T. and Wand, M.P. Variational Bayesian Inference for Parametic and Nonparametric Regression with Missing Data
08-10 Chacon, J.E.; Duong, T. and Wand, M.P. Asymptotics for General Multivariate Kernel Density Derivative Estimators.
09-10 Y.-X. Lin, V. Baladandauthapani, V. Bonato and K.-A. Do Estimating Shared Copy Number Aberrations for Array CGH Data: the Linear-Median Method
10-10 Y -X. Lin, V. Baladandayuthapani,V. Bonato and K.-A. DO Supplementary material for Estimating Copy Numbers for Shared Array CGH Data: the Linear-Median Method
11-10  David Griffiths, Martin Bunder, Chandra Gulati, Takeo Onizawa The Probability of an Out of Control Signal from Nelson's Supplementary Zig-Zag Test
12-10 Carole Birrell, Yan-Xia Lin, David G. Steel Parameter estimation and naive bias for a seasonally adjusted aggregate series of different lengths using univariate and multivariate approaches
13-10 B.M. Brown, Thomas Suesse and Von Bing Yap Wilson confidence intervals for the two-sample log-odds-ratio in stratified 2 x 2 contingency tables
14-10 Thomas Suesse, Ivy Liu Mantel-Haenszel Estimator s of Odds Ratios for Stratified Dependent Binomial Data
15-10 Brian Cullis, David Butler, Daryl Mares, Kolumbina Mrva and Hai Yunn Law Combined analysis of 08/09 and 2010 experiments
16-10 James O. Chipperfield and David G. Steel Multivariate Random Effect Models with complete and incomplete data
17-10 Stephen Beare, Ray Chambers, Scott Peak, Jennifer M Ring Accounting for Spatiotemporal Variation of Rainfall Measurements when Evaluating Ground-Based Methods of Weather Modification
18-10 Klairung Samart and Ray Chambers Fitting Linear Mixed Models Using Linked Data
19-10 Hukum Chandra, Nicola Salvati and U.C. Sud Disaggregat-level estimates of indebtedness in the state of Uttar Pradesh in India-an application of small area estimation technique
20-10 Nicola Salvati, Hukum Chandra and Ray Chambers Model Based Direct Estimation of Small Area Distributions
21-10 Hukum Chandra, Nicola Salvati, Ray Chambers and Nikos Tzavidis Small Area Estimation under Spatial Nonstationarity
22-10 Gunky Kim and Raymond Chambers Regression analysis for longitudinally linked data
23-10 Thomas Suesse Marginalized Curved Exponential Random Graph Models
24-10 Sandy Burden, Yamine Probst, David Steel and Linda Tapsell The impact of complex survey design on prevalence estimates of intakes of food groups in the Australian National Children's Nutrition and Physical Activity Survey
25-10 Bothaina Bukhatowa, Anne Porter and Mark Nelson Emulating the Best Technology in Teaching and Learning Mathematics: Challenges Facing Libyan Higher Education

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 Adaptive Inference for Multi-Stage Survey Data
02-09 Samworth, R.J. and Wand, M.P. Asymptotics and Optimal Bandwidth Selection for Highest Density Region Estimation
03-09 Naumann, U., Luta, G. and Wand, M.P The curvHDR Method for Gating Flow Cytometry Samples
04-09 Ormerod, J.T. and Wand, M.P. Gaussian Variational Approximate Inference for Generalized Linear Mixed Models
05-09 Alkadiri, M., Carroll, R.J. and Wand, M.P. Marginal Longitudinal Semiparametric Regression via Penalized Splines
06-09 Hall, P., Ormerod, J.T. and Wand, M.P. Theory of Gaussian Variational Approximation for a Generalised Linear Mixed Model
07-09 Ormerod, J.T. and Wand, M.P. Explaining Variational Approximations
08-09 Chacon, J.E., and Duong, T. and Wand, M.P. Asymptotics for General Multivariate Kernel Density Derivative Estimators
09-09 Nikos Tzavidis, Monica Pratesi, Ray Chambers Small Area Estimation Via M-quantile Geographically Weighted Regression
10-09 Nikos Tzavidis, Stefano Marchetti and Ray Chambers Robust Estimation of Small Area Means and Quantiles
11-09 Ray Chambers, Hukum Chandra, Nikos Tzavidis On Bias-Robust Mean Squared Error Estimation for Pseudo-Linear Small Area Estimators
12-09 Ray Chambers, Nikos Tzavidis, Nicola Salvati Borrowing strength over space in small area estimation: Comparing parametric, semi-parametric and non-parametric random effects and M-quantile small area models
13-09 Dr George Sofronov Spatial small area estimation: Comparison of different approaches
14-09 Stephen Beare, Ray Chambers, Scott Peak Statistical Modelling of Rainfall Enhancement
15-09 Hukum Chandra, Ray Chambers, Nicola Salvati Small Area Estimation in Proportions in Business Surveys
16-09 R Chambers, H Chandra, N Salvati and N Tzavidis Outliner Robust Small Area Estimation
17-09 Gunky Kim and Raymond Chambers Regression analysis under incomplete linkage
18-09 Ray Chambers, James Chipperfield, Walter Davis, Milorad Kovacevic Inference Based on Estimating Equations and Probability-Linked Data
19-09 Nicola Salvati, Hukum Chandra, M. Giovanna Ranalli and Ray Chambers Small Area Estimation Using a Nonparametric Model Based Direct Estimator
20-09 Gandhi Pawitan, David G. Steel Exploring the MAUP from a spatial perspective
21-09 Heni Puspaningrum, Yan-Xia Lin, Chandra Gulati Finding the Optimal Pre-Set Boundaries for Pairs Trading Strategy Based on Cointegration Technique
22-09 Raed Alzghool, Yan-Xia Lin and Song Xi Chen Asymptotic Quasi-likelihood Based on Kernel Smoothing for Multivariate Heteroskedastic Models with Correlation
23-09 Yan-Xia Lin Visually Identifying Potential Domains for Change Points in Generalized Bernoulli Processes: an Application to DNA Segmental Analysis
24-09 Norhayati Baharun and Anne Porter Teaching Statistics Using a Blend  Approach: Integrating Technology-based Resources
25-09 Norhayati Baharun and Anne Porter Learning Designs to Engage and Support Learners
26-09 Norhayati Baharun and Anne Porter Removing the Angst from Statistics
27-09 Norhayati Baharun and Anne Porter The Use of Technology to Support Student Learning

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 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

Books

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:

  • 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 “R Tips” throughout.
  • Features detailed examples and applications in end-of-chapter Labs.
  • Features “Technical Notes” 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 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.

Read further details of the book, or purchase 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 staisticians, and researchers and practicioners in the field of applied mathematics, engineering, and the environmental and health sciences.

Further details are available at Wiley Press.