Trinity College Dublin


Trinity College Dublin By using this website you consent to the use of cookies in accordance with the Trinity cookie policy. For more information on cookies see our cookie policy.

Profile Photo

Dr. Brett Houlding

Assistant Professor (Statistics)

 


Originally from Mansfield, UK, I joined Trevelyan College, Durham University, as an undergraduate in 2000. In 2004 I graduated as a Master of Mathematics (MMath I class honours) and enrolled as a PhD student under the supervision of Prof. Frank Coolen. In 2005 I became a Resident Tutor of Trevelyan College and in 2008 I graduated from Durham as a Doctor of Philosophy (PhD). I joined the Center for Telecommunications Value-Chain Research group in Trinity College Dublin as a postdoctoral researcher in May 2008, and then in May 2009 I joined the new STATICA project of Prof. Wilson. In November 2011 I was awarded a 5 year lecturing position in the Discipline of Statistics.
  Biological Modeling   Biostatistical methods   Criminal Statistics   Data Analysis   Decision Sciences   Forensic Law   Game Theory   Operations Research   Optimization   Probability   RELIABILITY THEORY   Risk Factors/Analysis   Signal processing   Simulation and modelling transportation networks   Statistical and simulation methodologies   Statistics   Stochastic Processes   Utilities
Details Date
Reviewer for following Journals/Conferences: AISTATS; Quality Reliability Engineering International; International Journal of Approximate Reasoning; Journal of Statistical Planning and Inference.
Costello MJ, Houlding B, Wilson SP, As in other taxa, relatively fewer beetles are being described by an increasing number of authors: response to Löbl and Leschen, Systematic Entomology, 2014, p5 , Journal Article, PUBLISHED  URL
Costello MJ, Houlding B, Joppa, LN, Further evidence of more taxonomists discovering new species, and that most species have been named: response to Bebber et al. (2014), New Phytologist, 202, (3), 2014, p739 - 740, Journal Article, PUBLISHED  URL
Costello M, Wilson SP, Houlding B, More Taxonomists Describing Significantly Fewer Species per Unit Effort May Indicate That Most Species Have Been Discovered, Systematic Biology, 62, (4), 2013, p616 - 624, Journal Article, PUBLISHED  DOI  URL
Houlding B, and Haslett J, Scheduling Parallel Conference Sessions: An Application of a Novel Hybrid Clustering Algorithm for Ensuring Constrained Cardinality, Journal of Applied Statistics, 40, (5), 2013, p961 - 971, Journal Article, PUBLISHED  DOI
B. Houlding, F.P.A. Coolen, Nonparametric Predictive Utility Inference, European Journal of Operational Research, 221, 2012, p222 - 230, Journal Article, PUBLISHED  TARA - Full Text
Mark Costello, Simon P. Wilson and Brett Houlding, Predicting total global species richness using rates of species description and estimates of taxonomic effort, Systematic Biology, 61, (5), 2012, p871 - 883, Journal Article, PUBLISHED  TARA - Full Text  DOI
Costello MJ, Wilson S, Houlding B, Predicting total global species richness using rates of species description and estimates of taxonomic effort., Systematic biology, 61, (5), 2012, p871-83 , Journal Article, PUBLISHED  DOI
Brett Houlding and Simon P. Wilson, Considerations on the UK Re-Arrest Hazard Data Analysis (How Model Selection Can Alter Conclusions for Policy Development), 58th Congress of the International Statistical Institute, Dublin, Ireland, 21/08/11 - 26/08/11, 2011, Notes: [Online publication], Conference Paper, PUBLISHED  TARA - Full Text  URL
Coolen FPA, Houlding B, & Parkinson G, Considerations on Jury Size and Composition using Lower Probabilities, Journal of Statistical Planning and Inference, 141, (1), 2011, p382 - 391, Journal Article, PUBLISHED  DOI
B. Houlding and F.P.A. Coolen, Adaptive utility and trial aversion, Journal of Statistical Planning and Inference, 141, (2), 2011, p734-747 , Journal Article, PUBLISHED  TARA - Full Text
  

Page 1 of 2

  

I have many areas of research interest within Statistics and Operations Research, including both application and theoretical development. Recent work has focused on estimating the total numbers of species of different taxa, which has been joint work with Simon Wilson of Trinity College and Mark Costello of the Leigh Marine Laboratory, University of Auckland. Currently this is based on data from the dates of first reporting of different species, starting with the initial work of Linnaeus. This question is important in discussion of species extinction rates and biodiversity. We form part of a global team, including researchers in Canada, France, the United States and Australia, that is evaluating the many different methods of estimating species numbers. Other/General Interests: I have a strong interest in the foundations of statistics, and specifically, normative Bayesian statistical decision theory, with previous work in this area focusing on the generalisation of adaptive utility that permits uncertainty in not only a decision maker's beliefs, but also their preferences. A brief outline of work conducted in this area is summarised: Theories of sequential decision making have been developed to consider coherent decision making strategies over several decision epochs when actual decision outcomes are a priori uncertain. Such theories have many areas of application, e.g., experimental design and policy development, with contributions being made by researchers in mathematics, statistics, economics, philosophy and psychology. Traditionally, all uncertainties are modelled via precise probabilities, and all preferences over decision outcomes via known utilities. It is recognised, however, that indeterminacy may complicate assessment of such probabilities and/or utilities, especially when the decision maker has little or no prior experience within the context of the decision problem under consideration. As such, recent attention has focused on developing decision making algorithms that are able to accommodate imprecise probabilities (where precise values are replaced by intervals) and/or uncertain utilities (where fixed quantities are replaced by random variables). This then allows indeterminacy to be taken into account, generalising classical theories of decision making, and permitting the creation of robust Bayesian methods for within statistical decision theory. However, further implications are the effects these generalisations have on the axiomatic foundations of the theory, and also on traditional decision theory concepts such as value of information and risk aversion. Finally, I also have a growing interest in the use of statistical arguments for policy development.