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Thomas Suesse
BSc+MSc (Friedrich-Schiller
University of Jena,Germany) |
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Senior Lecturer at the |
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Email: tsuesse@uow.edu.au |
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Phone: +61 2 4221 4173 |
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Fax: +61 2 4221 5474 |
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Room: 39C.191
Postal Address
Biography
Research Interests
Publications
PhD Projects
R Code
Biography
I
completed my M.Sc. (Dipl.-Math.) degree in mathematics at the
Friedrich-Schiller-University (FSU) of Jena, Germany, in 2003. My thesis focused on multiple and global testing procedures.
From 2003-2005, I worked as a research fellow at the Institute of Medical
Statistics, Informatics and Documentation (IMSID), also FSU. My work focused on
modelling of thalamic brain activity by forced and coupled relaxation
oscillators.
In 2005 I went to Victoria University of Wellington (VUW), New Zealand, to
start my PhD study under supervision of Dr Ivy Liu. In 2008 I finished the PhD
in Statistics entitled "Analysis and Diagnostics of Categorical Variables
with Multiple Outcomes". After a short stay at the University of NSW,
where I worked as a postdoctoral research fellow on the statistical methodology
for the validation of surrogate biomarkers, in 2009 I started working as a
research fellow at the Centre for Statistical and Survey Methodology (CSSM) at
the University of Wollongong, mainly working on the modelling of social
networks and investigating its use for survey methodology. I was appointed as
lecturer in statistics in 2011 at the School of Mathematics and Applied
Statistics (SMAS) and was promoted to senior lecturer in 2015. I am a member of
National Institute of Applied Statistics Research Australia (NIASRA).
Research Interests
Categorical
Data Analysis
Survey
Methodology
Social
Networks
Finite
Mixture Models
Synthetic
Population Generation
Statistical
Computing
Smooth
Tests
Spatial
Statistics/Modelling
Engineering
Education
Publications
Book
Suesse T. (2010). Analysis
and Diagnostics of Categorical Variables with Multiple Outcomes. LAP LAMBERT Academic Publishing. ISBN-13: 978-3-8383-1067-1.
ISBN-10: 3838310675. 224 pages. https://www.morebooks.de/store/gb/book/analysis/isbn/978-3-8383-1067-1
Book Chapter
Suesse, T., Rayner, J. & Thas, O.(2015). Smooth tests
of fit for Gaussian mixtures. In B. Lausen, S.
Krolak-Schwerdt & M. Böhmer (Eds.), Data Science, Learning by Latent
Structures, and Knowledge Discovery (pp. 133-142). Germany: Springer
Berlin Heidelberg.
Journal Articles
Suesse, T.,
Liu I. (2019) Mantel–Haenszel
estimators of a common odds ratio
for multiple response data.
Statistical Methods & Applications 28(1), 57-76.
Suesse T., Zammit-Mangion, A.
(2019). Marginal Maximum Likelihood Estimation of Conditional
Autoregressive Models with Missing
Data. STAT. 8(1), e226.
Barthélemy, J., Suesse, T. (2018). mipfp: A
R Package for Multidimensional Array Fitting and Simulating Multivariate
Bernoulli Distributions. Journal of Statistical Software, accepted August 2017.
Suesse, T., Chambers R. (2018). Using
Network Information for Survey Estimation. Journal of
Official Statistics, 34(1). 181-209, 2018.
Suesse T. (2018) Marginal maximum likelihood estimation
of SAR models with missing data, Computational Statistics and Data Analysis,
120, 98-110.
Suesse, T. (2018). Estimation
of spatial autoregressive models with measurement
error for large data sets.
Computational Statistics 33 (4),
1627-1648.
Suesse, T., Rayner, J.C.W., Thas, O. (2017). Assessing the fit of finite
mixture distributions. Australian and New Zealand Journal of Statistics,
59(4), 463-483.
Suesse, T., Namazi-Rad,
M.-R., Mokhtarian, P., Barthélemy,
J. (2017). Estimating Cross-Classified Population Counts of Multidimensional
Tables: An Application to Regional Australia to Obtain Pseudo-Census Counts.
Journal of Official Statistics 33(4),1021-1050.
Nikolic, S., Suesse, T. F., McCarthy,
T. J. & Goldfinch, T. L. (2017). Maximising resource allocation
in the teaching laboratory: understanding student evaluations of teaching
assistants in a team-based teaching format. European Journal of
Engineering Education, 42 (6), 1277-1295.
Suesse, T. & Zammit-Mangion,
A. (2017). Computational aspects of the EM algorithm for spatial econometric models
with missing data. Journal
of Statistical Computation and Simulation, 87 (9), 1767-1786.
Jamali, S.
S., Suesse, T., Jamali,
S., Mills, D. J. & Zhao, Y. (2017). Mechanism of ionic
conduction in multi-layer polymeric films studied via electrochemical
measurement and theoretical modelling. Progress in Organic
Coatings, 108, 68-74.
Cressie, N., Burden, S., Davis,
W., Krivitsky, P. N.
, Mokhtarian, P., Suesse,
T. & Zammit-Mangion, A. (2015). Capturing multivariate spatial dependence:
model, estimate and then predict. Statistical Science: a review
journal, 30 (2), 170-175.
Suesse, T. & Liu,
I. (2013). Modelling
strategies for repeated multiple response data. International Statistical
Review, 81 (2), 230-248.
Suesse, T. F. (2012). Marginalized exponential random
graph models. Journal of Computational and Graphical Statistics, 21
(4), 883-900.
Suesse, T. & Liu,
I. (2012). Mantel-Haenszel estimators of odds ratios for stratified
dependent binomial data. Computational Statistics and Data
Analysis, 56 (9), 2705-2717.
Brown,
B., Suesse, T. & Yap,
V. (2012). Wilson confidence intervals for the two-sample
log-odds-ratio in stratified 2 × 2 contingency tables. Communications in
Statistics - Theory and Methods, 41 (18), 3355-3370.
Liu, I., Mukherjee, B., Suesse, T. F., Sparrow, D. & Park, S. Kyun. (2009). Graphical diagnostics to check model misspecification for the proportional odds regression model.Statistics in Medicine, 28 (3), 412-429.
Liu,
I. & Suesse, T. (2008). The
analysis of stratified multiple responses. Biometrical Journal: journal of
mathematical methods in biosciences, 50 (1), 135-149.
Suesse, T. F. & Liu,
I. (2008). Diagnostics for Multiple Response Data. Tatra Mountains Mathematical Publications, 39 105-113.
Haueisen,
J., Leistritz, L., Suesse, T. F., Curio, G. & Witte,
H. (2007). Identifying mutual information transfer in the brain with
differential-algebraic modeling: Evidence for fast oscillatory coupling between
cortical somatosensory areas 3b and 1. Neuroimage, 37 (1),
130-136.
Leistritz, L., Putsche, P., Schwab,
K., Hesse, W., Suesse, T., Haueisen,
J. & Witte, H. (2007). Coupled oscillators for modeling and analysis of EEG/MEG oscillations. Biomedizinische
Technik, 52 (1), 83-89.
Leistritz, L., Suesse,
T., Haueisen, J., Hilgenfeld, B. & Witte,
H. (2006). Methods
for parameter identification in oscillatory networks and application to
cortical and thalamic 600 Hz activity. Journal of
Physiology-Paris, 99 (1), 58-65.
Hemmelmann,
C., Horn, M., Suesse, T., Vollandt, R. & Weiss, S. (2005). New concepts of multiple
tests and their use for evaluating high-dimensional EEG data. Journal of
Neuroscience Methods, 142 (2), 209-217.
Witte, H., Putschke, P., Schwab, K., Eiselt,
M., Helbig, M. & Suesse, T. F. (2004). On the spatio-temporal
organisation of quadratic phase-couplings in trac alternant EEG pattern in
full-term newborns. Clinical Neurophysiology, 115 2308-2315.
Hemmelmann, C., Horn, M., Reiterer, S., Schack, B., Suesse, T. F. & Weiss, S. (2004). Multivariate tests for the
evaluation of high-dimensional EEG data. Journal of Neuroscience Methods, 139
(1), 111-120.
Suesse, T. F., Haueisen,
J., Hilgenfeld, B., Leistritz,
L. & Witte, H. (2004). Oszillatormodelle zur
Beschreibung von thalamischer
und kortikaler 600 Hz Aktivitt
(Engl.:
Oscillator models describing cortical and thalamic 600Hz activity). Biomedizinische Technik
Supplement, 49, 322-323.
Invited Keynote Speaker Presentations
Suesse T. and Chambers, R. (2014). Using Social Network
Information in Survey Estimation. “Computational Methods
for Survey and Census Data in the Social Sciences” A workshop for statisticians
and social scientists. 20-21 June Montreal, Canada.
Suesse T. and Chambers, R. (2013). Using Social Network
Information in Survey Estimation. Graybill Conference: Modern Survey Statistics.
9- 12 June Fort Collins, Colorado, USA.
Invited presentations
Suesse T. and Liu I. (2011). Modelling Strategies for Repeated Multiple Response Data. NZ Statistics Conference. University of
Auckland, Auckland, New Zealand.
Suesse T. and
Brown B. (2011). Wilson
Confidence Intervals for Stratified 2 by 2 Tables. 4th ASEARC Conference.
University at Western Sydney, Sydney, Australia.
Conference Publications
Nikolic, S., Suesse, T., Goldfinch,
T. & McCarthy, T. (2015). Relationship between learning in the
engineering laboratory and student evaluations. Proceedings of the
Australasian Association for Engineering Education Annual Conference (pp. 1-9).
Mokhtarian, P., Namazi-Rad, M., Ho, T. Kin. & Suesse, T. (2013). Bayesian nonparametric reliability analysis for a railway system at component level. IEEE International Conference on Intelligent Rail Transportation (ICIRT) (pp. 197-202). China: The Institute of Electrical and Electronics Engineers Inc.
Suesse, T. (2012). Estimation in autoregressive
population models. The Fifth Annual ASEARC Research Conference: Looking to
the future (pp. 11-14). Wollongong NSW: University of Wollongong.
Software
Barthelemy,
J. & Suesse, T. F. (2016).
Package mipfp: multidimensional iterative proportional fitting and alternative
models v3.0 Web. Online: CRAN.
PhD
Projects
Social Networks
Networks,
or mathematical graphs, are an important tool for representing relational data,
i.e. data on the existence, strength and direction of relationships between
interacting actors. Types of actors include individuals, firms and countries. Modeling networks has become more and more important, in
particular caused by negative developments in terrorists networks over the past
decade, and the currently most widely used class of models are Exponential
Random Graph Models (ERGMs). This model approach is useful to explain the
underlying generating structure of these data, but is limited in many ways. The
PhD project would focus on developing other model approaches that overcome the
limitations of ERGMs, for example exploring the use of marginal and
transitional models for network data, among others. It also includes
theoretical aspects, as consistency of model parameters under non-informative
sampling and many more aspects.
Categorical Data Analysis
A common
model approach to multivariate binary data is to apply a log-linear model.
Log-linear models are useful for describing the joint distribution, but not
useful for describing the marginal distribution. A simpler and more effective
approach is to apply a generalized linear model (GLM), but it does not account
for the dependence of the binary observations. A standard approach that
accounts for this dependence is to use generalized estimating equations (GEE).
Another less widely known approach is to apply a log-linear model and to
constrain the model by a GLM. However current fitting techniques using the
iterative proportional fitting (IPF) algorithm are infeasible for large
cluster-sizes. The PhD project would focus on the use of
Markov-Chain-Monte-Carlo (MCMC) techniques to overcome the limitations of the
IPF algorithm. The standard assumption for the model approach is to have equal
cluster sizes, the project would also focus on overcoming this limitation,
considering smaller cluster sizes as clusters with missing data.
Another related topic would focus on the use of a hybrid method combining generalized mixed models (GLMMs) and marginal models (GLMs). The investigator might be interests in a marginal model that still accounts for some of the variations of model parameters, but not to all. For example in a multi-centre clinical trial, multiple observations might be recorded for each patient and the standard treatment would be compared to a new treatment. Then neither the marginal nor the GLMM approach would be suitable. The PhD project would explore effective model fitting techniques and explore usefulness of such an approach in other applications.
Variance Component Estimation and Testing for
Distribution for Mixture Distributions
In (model-based) cluster analysis, mixture distributions are a common tool to model clusters, where each cluster is represented by one multivariate normal distribution, where the mean and variance of that particular multivariate normal characterise important properties of this cluster, as location and scale. Parameter estimation is often achieved by maximum likelihood but resulting in biased variance estimates of the multivariate normal, which might result in incorrect conclusions for cluster analysis. The aim in this project is to obtain unbiased variance estimates. Another issue with model based clustering is checking the validity of the distributional assumptions. This project aims at using smooth tests to check for any distribution of the component densities.
Spatial Autoregressive Models
Estimation
of Spatial Autoregressive Models (SAR) under the Presence of Missing Data is not
straightforward due the model specification of the precision matrix instead of
the covariance matrix. Efficient and fast maximum likelihood (ML) estimation of
SAR models has been considered by Suesse & Zammit-Mangion (2017) and Suesse
(2018). The methods require the knowledge of the full contiguity matrix of all
units (of the observed and missing) and that missingness
is at random. Often spatial models also include other errors, such as the
measurement errors or a more complex multi-level (hierarchical) structure.
Suesse (2017) has considered computational efficient ML estimation for large
data sets.
The aim of this PhD project is to extent the
computational methods to more complex spatial autoregressive models (for
example SARAR model among others and models with additional random effects),
but also to consider methods that do not require the knowledge of all locations
and also do not require that data are missing at random. There are a variety of
research questions that can be addressed, also depending on the candidate’s
background and knowledge.
R
Code
Smooth Tests for Finite Mixture Distributions
Estimation of SAR Models with Measurement Error
Marginal Maximum Likelihood Estimation of SAR
models with Missing Data
Marginal Maximum Likelihood Estimation of CAR
models with Missing Data
Fitting Marginalized Exponential Random Graph Models via GEE