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Suesse, Thomas F. Dr.

Senior Lecturer

  • National Institute for Applied Statistical Research Australia
  • Faculty of Engineering and Information Sciences
  • School of Mathematics and Applied Statistics

Overview


Thomas Suesse has completed his M.Sc. (Dipl.-Math.) degree in mathematics at the Friedrich-Schiller-University (FSU) of Jena, Germany, in 2003. His thesis focused on multiple and global testing procedures. From 2003-2005, he worked as a research fellow at the Institute of Medical Statistics, Informatics and Documentation (IMSID), also FSU. His work focused on modelling of thalamic brain activity by forced and coupled relaxation oscillators.

In 2005 he went to Victoria University of Wellington (VUW), New Zealand, to start his PhD study under supervision of Dr Ivy Liu. In 2008 he finished his PhD in Statistics entitled "Analysis and Diagnostics of Categorical Variables with Multiple Outcomes". After a short stay at the University of NSW, where he worked as a postdoctoral research fellow on the statistical methodology for the validation of surrogate biomarkers, in 2009 he 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.

Professional Activities:

Member of the Statistical Society of Australia

Awards:

2008 VUW PhD thesis submission award (5000$ NZ)
2005-2008 VUW Postgraduate Scholarship (20000$ NZ per annum plus tuition fees), tenure 3 years

Top Publications


Research Overview


  • Categorical Data Analysis; Survey Methodology; Social Networks.

Available as Research Supervisor

Selected Publications


Available as Research Supervisor

Potential Supervision Topics


  • Estimation for Spatial Auto-regressive Models under the presence of Missing Data

    Spatial auto-regressive (SAR) models  are popular models for a finite number of spatially correlated units. Estimation, for example via Maximum likelihood is well established for fully observed units.  However when missing units are present, estimation of model parameters referring to all units is not straightforward. In the project, estimation of a range models including prediction will be considered under the presence of missing units. There are many SAR related models and the same estimation problem applies to all of them.

  • Estimation of Effects referring to Latent Variables in Poisson Mixed Models

    A Poisson Mixed Model is a popular model for counts. When the explanatory variables are not observed for locations of the response variables but for other locations, estimation is complicated. This project will consider various estimation methods to estimate the effects referring to this "unobserved" explanatory variable. 

     

  • Variance Estimation in Mixture Models

    Mixture models are popular models for classification. The underlying assumption is often that the component distributions are multivariate normal. Estimation of such a mixture is often via maximum likelihood. Then the variance parameters are often underestimated. In this project, you aim at improving upon variance estimates not only for improved efficiency, but also aiming for improved classification rate.

  • 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.

  • Modelling of 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.

Advisees


  • Graduate Advising Relationship

    Degree Research Title Advisee
    Doctor of Philosophy Computational Methods for the Analysis of 'Big Data' - Linear Mixed Model With Application to Genomics Mazur, Luke
    Doctor of Philosophy Does neighbourhood green space promote better perinatal health outcomes? Investigating mechanisms, differential effects by green space type, and potential effect modifiers by other features of the built environment. Akaraci, Selin

Top Publications


Research Overview


  • Categorical Data Analysis; Survey Methodology; Social Networks.

Selected Publications


Potential Supervision Topics


  • Estimation for Spatial Auto-regressive Models under the presence of Missing Data

    Spatial auto-regressive (SAR) models  are popular models for a finite number of spatially correlated units. Estimation, for example via Maximum likelihood is well established for fully observed units.  However when missing units are present, estimation of model parameters referring to all units is not straightforward. In the project, estimation of a range models including prediction will be considered under the presence of missing units. There are many SAR related models and the same estimation problem applies to all of them.

  • Estimation of Effects referring to Latent Variables in Poisson Mixed Models

    A Poisson Mixed Model is a popular model for counts. When the explanatory variables are not observed for locations of the response variables but for other locations, estimation is complicated. This project will consider various estimation methods to estimate the effects referring to this "unobserved" explanatory variable. 

     

  • Variance Estimation in Mixture Models

    Mixture models are popular models for classification. The underlying assumption is often that the component distributions are multivariate normal. Estimation of such a mixture is often via maximum likelihood. Then the variance parameters are often underestimated. In this project, you aim at improving upon variance estimates not only for improved efficiency, but also aiming for improved classification rate.

  • 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.

  • Modelling of 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.

Advisees


  • Graduate Advising Relationship

    Degree Research Title Advisee
    Doctor of Philosophy Computational Methods for the Analysis of 'Big Data' - Linear Mixed Model With Application to Genomics Mazur, Luke
    Doctor of Philosophy Does neighbourhood green space promote better perinatal health outcomes? Investigating mechanisms, differential effects by green space type, and potential effect modifiers by other features of the built environment. Akaraci, Selin
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Research Areas