Royal Statistical Society North Eastern Local Group

Programmes for previous years

• 2006/2007
• 2005/2006
• 2004/2005
• 2003/2004
• 2002/2003
• 2001/2002

Programme 2006/2007

• Wednesday 18th October 2006
  Speaker: Dr James Carpenter (Medical Statistics Unit, London School of Hygiene and Tropical Medicine)
  Title: Sensitivity analysis after multiple imputation under missing at random: a weighting approach
  Venue: "The Buttery", L401, Merz Court, Newcastle University
  Time: 3.30 pm (note this earlier time!)
  Special Info: Seaonal Refreshments after the talk!
  Synopsis: Multiple Imputation (MI) is now well established as a flexible, general, method for the analysis of data sets with missing values. Most implementations assume the missing data are `Missing At Random' (MAR), i.e. given the observed data, the reason for the missing data does not depend on the unseen data. However, although this is a helpful and simplifying working assumption, it is unlikely to be true in practice. Assessing the sensitivity of the analysis to the MAR assumption is therefore important.

  However, there is very limited MI software for this. Further, analysis of a data set with missing values that are Not Missing At Random (NMAR) is complicated by the need to extend the MAR imputation model to include a model for the reason for dropout. Here, we propose a simple alternative. We first impute under MAR and obtain parameter estimates for each imputed data set. The overall NMAR parameter estimate is a weighted average of these parameter estimates, where the weights depend on the assumed degree of departure from MAR. In some settings, this approach gives results that closely agree with joint modelling as the number of imputations increases. In others, it provides ball-park estimates of the results of full NMAR modelling, indicating the extent to which it is necessary and providing a check on its results.

  We illustrate our approach with a small simulation study, and the analysis of data from a trial of interventions to improve the quality of peer review.

• Wednesday 6th December 2006
  Speaker: Professor Heather Cordell, Institute of Statistical Genetics, Newcastle University
  Title:Multiple Imputation Methods for Genetic Association Analysis of Complex Traits
  Venue: Newcastle
  Time: 3.30pm (note this earlier time!)
  Synopsis:To test for association between a disease and a set of linked genetic markers, or to estimate relative risks of disease, several different methods have been developed. Many methods for family or case/control data require that individuals be genotyped at the full set of markers and that phase-known genotypes (i.e. the combination of haplotypes present in an individual) can be reconstructed. Individuals with missing data are excluded from the analysis. This can result in an important decrease in sample size and a loss of information. A possible solution to this problem is to use missing-data likelihood methods. We propose an alternative approach, namely the use of multiple imputation. Briefly, this method consists in estimating from the available data all possible phased genotypes and their respective posterior probabilities. These posterior probabilities are then used to generate replicate imputed data sets via a data augmentation algorithm. We performed simulations to test this approach for case/control and case/parent trio data and we found that the multiple imputation procedure generally gave unbiased parameter estimates with correct type 1 error and confidence interval coverage. Multiple imputation had some advantages over missing data likelihood methods with regards to ease of use and model flexibility.
• Thursday 18th January 2007
  Speaker: Dr Baibing Li, Loughborough University
  Title: Tissue classification with gene expression profiles: a clustering-function-based approach
  Venue: Newcastle
  Time: 4.00pm
  Synopsis: In recent years, it has become commonplace for researchers to perform cluster analysis to identify patterns in gene expression data, thanks to the DNA microarray technology which has now made it possible to investigate thousands of gene expression data simultaneously. This talk will review the recently developed clustering method, the clustering-function-based method, and discuss its links to the Fisher linear discriminant analysis, the model-based clustering method, and regression analysis. To illustrate this new methodology, two case studies for clustering gene expression data are investigated. The results are compared with that obtained using conventional clustering methods.
• Wednesday 7th March 2007
  Speaker: Paul Blackwell, Statistics Department, Sheffield University
  Title: Environmental Series observed at Uncertain Times
  Venue: CM101, Dept of Mathematics, Durham
  Time: 17.00
  Synopsis: In this talk, I will introduce some statistical problems involving "time series" where the actual times of observations are not precisely known. The main example that I will discuss is the construction of the radiocarbon calibration curve, necessary for accurate radiocarbon dating. Recent points on the curve can be obtained from carbon samples whose ages are known fairly accurately, from the counting of tree rings; older points have uncertainty in both their radiocarbon levels and their ages, from measurement or counting error. I will talk about the Bayesian methods which were developed to tackle this complexity and which led to the latest international standard curves in 2004. A second example relates to the dating of measurements from Arctic and Antarctic ice cores. These cores hold crucial information about the past environment, but interpreting them depends on the complex and uncertain relationship between the depth of the ice and its age. A new project, in collaboration with the British Antarctic Survey, involves developing methods to date ice cores while making due allowance for uncertainty in layer counting, past rates of ice accumulation and ice flow and compression.
  
• Thursday 10th May 2007
  Speaker: Adrian Bowman, Department of Statistics, University of Glasgow
  Title: The use of additive models in environmental modelling
  Venue: CM101 Durham
  Time: 16.30
  Synopsis:Additive models extend standard regression methods by allowing very flexible, but smooth, relationships between variables of interest. The basic ideas and potential benefits of these models will be discussed and illustrated on data from several different environmental studies, including water quality in the River Clyde and SO2 pollution over Europe. The technical aspects of the talk will focus on appropriate methods of inference, including the need to incorporate suitable forms of spatial and temporal correlation.

Programme 2005/2006

• Tuesday 11th October 2005
  Speaker:Professor Christl Donnelly (Dept of Infectious Disease Epidemiology, Imperial College London)
  Title: Statistical and epidemiological analysis of the SARS epidemic in Hong Kong
  Venue: L401 (the "Buttery"), Merz Court, Newcastle; contact Malcolm Farrow, tel 0191 515 2762
  Time: 5.30pm
  Synopsis: The rapid worldwide spread of SARS provided a major challenge to public health in 2003. The high fatality rate and the travel restrictions imposed to control its spread caused widespread anxiety. Data from Hong Kong will be presented together with the analysis used to estimate key epidemiological factors determining the spread of SARS and leading to its control.
• Thursday 10 November 2005
  Speaker: Professor John Haslett (Dept Statistics, Trinity College Dublin)
  Title: Bayesian palaeoclimate reconstruction
  Venue: CY91 (previously CG91), Durham; contact Jonathan Rougier, tel 0191 334 3111
  Time: 5.30pm
  Synopsis: We consider the problem of reconstructing prehistoric climates using fossil data extracted from lake sediment cores. Such reconstructions promise to provide one of the few ways to validate modern models of climate change. A hierarchical Bayesian modelling approach is presented and its use, inversely, is demonstrated in a relatively small but statistically challenging exercise, the reconstruction of prehistoric climate at Glendalough in Ireland from fossil pollen. This computationally intensive method extends current approaches by (a) explicitly modelling uncertainty and (b) reconstructing entire climate histories. The statistical issues raised relate to the use of compositional data (pollen) with covariates (climate) which are available at many modern sites but are missing for the fossil data. The compositional data arise as mixtures and the missing covariates have a temporal structure. Novel aspects of the analysis include:
  • a spatial process model for compositional data;
  • local modelling of lattice data;
  • the use, as a prior, of a random walk with long tailed increments;
  • a two-stage implementation of the Markov chain Monte Carlo approach;
  • and a fast approximate procedure for cross-validation in inverse problems.
  We present some details, contrasting its reconstructions with those generated by a method in use in the palaeoclimatology literature. We suggest that the method provides a basis for resolving important challenging issues in palaeoclimate research. We draw attention to several challenging statistical issues that need to be overcome.
• Tuesday 13 December 2005
  Speaker: Dr Jonathan Rougier (Dept Mathematical Sciences, Durham)
  Title: Uncertainties concerning future climate
  Venue: CY60 (previously CG60), Durham; contact John Little, tel 0191 334 3117
  Time: 5.30pm
  Synopsis: Our predictions for future climate, and in particular the ways in which it will respond to increasing levels of atmospheric greenhouse gases, depend to a large extent on the evaluation of computer-based climate simulators. Statisticians have been studying this type of inference for many years, under the general heading of Computer Experiments. Long-term climate prediction presents three particular challenges:
  1. The simulators are very expensive to evaluate;
  2. They are generally quite poor at representing climate at the regional level (which is where most of the interest resides);
  3. Lots of interesting things could happen in the next 100 years (eg in technology, economics and demographics).
  We look at the ways in which these problems contribute to our uncertainty about future climate.

  This is the traditional Christmas meeting, to be accompanied by cheese ("cheez" for vegans) and wine.

• Tuesday 7 February 2006
  Speaker: Dr William Browne (School of Mathematical Sciences, Univ. of Nottingham)
  Title: Classification of mass spectroscopy data using principal components analysis, Bayesian MCMC modelling and a deterministic peak finding algorithm.
  Venue: L401 (the "Buttery"), Merz Court, Newcastle; contact Darren Wilkinson, tel 0191 222 7320
  Time: 5.30pm
  Synopsis: In this talk we consider three approaches to classifying SELDI and MALDI mass spectroscopy datasets. Individual mass spectroscopy scans consist of a trace of ~14,000 values (intensities) at differing mass to charge ratios. Each individual scan belongs to one of m groups where individual groups may represent differing breast cancer lines and we wish to classify new scans to these groups. Due to the large number of variables (mass/charge ratios) associated with each scan we require data reduction techniques to give a group of derived variables that can be used for classification. We consider three techniques: Firstly principal components analysis (PCA) of the full scans to produce a smaller group of derived variables. Then two methods that take into account the fact that the scan is a sequential set of variables, and attempt to fit mixtures of scaled Gaussian distribution functions to the scan. We consider a deterministic algorithm and a model based MCMC method that reduce each scan to a series of (scaled) Gaussian peaks at locations that are common to all scans. The resulting heights of these peaks are then used in the classification. All three methods will be compared via cross-validation on two example datasets, one with 6 groups and one with 2 groups. This is joint work with Ian Dryden and Kelly Handley.
• Thursday 9 March 2006
  Speaker: Dr Barbel Finkenstadt (Department of Statistics, University of Warwick)
  Title: Stochastic modelling and inference for transcriptional gene regulation
  Venue: L401 (the "Buttery"), Merz Court, Newcastle; contact Malcolm Farrow, tel 0191 515 2762
  Time: 5.30pm
  Synopsis: A stochastic distributed delayed model is developed that explains the general molecular process of gene regulation. The model incorporates gene transcription, translation into protein and repression of gene expression by the nuclear form of the protein. Markov chain Monte Carlo methods are used to make inference about these unobserved processes and the unknown parameters of interest. We illustrate the methodology using a well understood gene regulatory network, namely the circadian clock, as an example.

  This is a joint meeting between the RSS-NE Group and BioNEt. Registration will be required; this can be done at the meeting itself. Register by emailing Natasha Taylor with name/affiliation.

• Thursday 18 May 2006
  Speaker: Professor Colin Aitken (Edinburgh University)
  Title: Evaluation of evidence
  Venue: CY60 (Chemistry Dept), Durham; contact Frank Coolen, tel 0191 334 3048
  Time: 5.30pm
  Synopsis: A review of probabilistic approaches to the evaluation of evidence will be given. This will include relative frequencies, significance probabilities, and likelihood ratios. Evidence evaluation for multivariate, two-level models, where the between-group distribution is not assumed to be normally distributed, will be discussed with the aid of graphical models.

  Reference will be made to some uses of statistical ideas in the courts. Cases discussed will include (a) the Daubert, Joiner and Kuhmo Tire trilogy from the US Supreme Court where the requirements for the admissibility of expert evidence were expressed, (b) the English Court of Appeal cases R v. Adams, D.J. and R. v. Doheny and Adams where comments about the role of statistics in legal argument were made, and (c) a more recent English Court of Appeal case R v. Grey where comments were made about the requirements for the use of frequency data. The relevance of these for recent cases concerning sudden explained deaths of infants will be discussed.

  • Daubert v. Merrell Dow Pharmaceuticals Inc., 509 U.S. 579,1993.
  • General Electric Co. v Joiner, 522 U.S. 136, 1997.
  • Kumho Tire Co. Ltd. v. Carmichael, 526 U.S. 137, 1999.
  • R v. Adams, D.J., 1997, 2 Cr. App. Rep. 4679.
  • R v. Doheny and Adams, 1 Cr. App. R. 369, 375, 1997.
  • R. v. Grey, CA(Crim Div), [2003] EWCA Crim 1001.

Programme 2004/2005

• Thursday 28th October 2004
  Speaker: Samer Kharroubi (University of Sheffield)
  Title: Estimating utilities from individual health preference data: a nonparametric Bayesian method
  Venue: Room CM101, University of Durham
  Time: 5.30 pm
  Synopsis: A fundamental benefit conferred by medical treatments is to increase the health-related quality of life (HRQoL) experienced by patients. Various descriptive systems exist to define a patient's health state, and we address the problem of assigning a HRQoL value to any given state in such a descriptive system. Data derive from experiments in which individuals are asked to assign their personal values to a number of health states. We construct a Bayesian model that takes account of various important aspects of such data. Specifically, we allow for the repeated measures feature that each individual values several different states, and the fact that individuals vary markedly in their valuations, with some people consistently providing higher valuations than others. We model the relationship between HRQoL and health state nonparametrically. We illustrate our method using data from an experiment in which 611 individuals each valued up to 6 states in the descriptive system known as the SF-6D. Although the SF-6D distinguishes 18,000 different health states, only 249 of these were valued in this experiment. We provide posterior inference about the HRQoL values for all 18,000 states.
• Thursday 9th December 2004
  Speaker: Chris Glasbey (Biomathematics and Statistics Scotland)
  Title: Quantitative image analysis
  Venue: Probably in room L401, Merz Court, University of Newcastle upon Tyne
  Time: 5.30 pm
  Synopsis: "Seeing is believing": sight is fundamental to our understanding of the world. This is as true in science as in everyday life. The collection of much scientific data is dependent on human vision. For example, the examination of samples under a microscope, estimation of sheep fatness from medical scanners, and visual identification of fish species are all forms of image analysis. We are superb at analyzing the images projected onto our retinas, using one third of our brains for vision. However, computers are being used more and more to automate and extend the potential of image analysis. Computers are better at extracting quantitative information from images than human observers: they can be more accurate and more consistent from day to day. Furthermore, computers may spare us from much tedious image interpretation. Several examples of the speaker's research will be used to illustrate these points.
• Thursday 27th January 2005
  Speaker: Richard Stevens (Diabetes Trials Unit, Oxford Centre for Diabetes Endocrinology and Metabolism)
  Title:Turning medical statistics into software: the "Risk Engine" project
  Venue: Room CM101, University of Durham
  Time: 5.30 pm
  Synopsis: The "UKPDS Risk Engine" is a computer program, based on survival models, that forecasts risk of heart disease in patients with diabetes. I will discuss the experience of publishing results as software as well as journal articles, and some of the unexpected statistical issues that have arisen.
• Friday 25th February 2005
  Bioinformatics Seminar organised jointly with BioNEt
  Venue: University of Newcastle upon Tyne, Room 2.29, Research Beehive, Old Library Building.
  Speaker: Charles Hodgman (Multidisciplinary Centre for Integrative Biology, University of Nottingham)
  Title:Towards predictive biology - a computational biologists perspective
  Time: 2.30pm
  Speaker: Dirk Husmeier (Biomathematics and Statistics Scotland, Edinburgh)
  Title:Detecting mosaic structures in DNA sequence alignments with factorial hidden Markov models
  Time: 4:00pm
  
• Thursday 12th May 2005
  Speakers: Matt Linsley, Shirley Coleman and Carolyn van Lottum (ISRU,University of Newcastle)
  Title: Novel and interesting collaborative applications of business and industrial statistics
  Venue: Stephenson Centre, University of Newcastle
  Time: 4.30pm
  Synopsis: ISRU have been solving problems across a wide range of industrial and business sectors over the last twenty years. Many well-known organisations, including government agencies, as well as numerous Small to Medium Enterprises have gained a commercial advantage through applying the unit's expertise.

  This presentation involves three case studies describing the unit's recent collaborative work with:

  1. a fellow department within the University of Newcastle upon Tyne
  2. other academic institutions throughout Europe via ENBIS and pro-ENBIS
  3. an international company that manufactures compressed air breathing equipment
  A number of different statistical applications are discussed such as Design of Experiments, Principal Components Analysis and Statistical Process Control.

Programme 2003/2004

• Thursday 11th December 2003
  Speaker: Jonathan Crook (School of Management, University of Edinburgh)
  Title: Reject inference techniques in credit scoring
  Venue: Room L401, Merz Court, University of Newcastle upon Tyne
  Time: 5.30 pm
  Synopsis: The estimated parameters of application scorecards may not be accurate estimates of the parameters that would relate to the population of all applicants because rejected applicants are usually excluded from the sample used to estimate these parameters. This work employs a sample that includes those who would normally be rejected in order to examine the extent to which the exclusion of rejected applicants undermines the predictive performance of a scorecard based only on accepted applicants and the extent to which reject inference techniques can remedy the influence of his exclusion. This analysis is applied to five acceptance thresholds. We also discuss the performance of the bivariate probit model with selection as an alternative reject inference technique.
• Thursday 22nd January 2004
  Speaker: Byron Morgan (University of Kent)
  Title: Models for yeast prions
  Venue: Room L401, Merz Court, University of Newcastle upon Tyne
  Time: 5.30 pm
  Synopsis: Yeast cells may contain proteins called prions, that behave like the mammalian prion PrP, implicated in CJD. This talk describes multi-type branching process modelling of the reproduction of yeast cells, and the distribution of prions in the cell population. The models are being developed in conjunction with current laboratory experiments.
• Thursday 11th March 2004
  Speaker: Brian Ripley (University of Oxford)
  Title: Statistical Methods need Software
  Venue: Room CG232, Science Site, University of Durham
  Time: 5.30 pm
  Synopsis: There is a chasm between writing a paper on a new method of data analysis for a journal and actually being able to use it for a real applied problem (and that may explain why the papers so rarely do supply such examples). For people trying to teach modern applied statistics the connection between theory and practice is vital. The missing bridge is suitable software, and the talk will cover some of the work I and collaborators have done to try to supply such a bridge (in particular the R project) and end with some examples of data analyses enabled by such software.
• Friday 23rd April 2004
  Speaker: Doug Montgomery (Arizona State University)
  Title: Designing Experiments: Some Adventures and Lessons Learned
  Venue: Continuing Professional Development room, Stephenson Centre, second floor Stephenson Building, University of Newcastle upon Tyne
  Time: 3.30 pm
  Synopsis: The most important phases of any experiment are pre-experimental planning and actual test conduct. If these activities are carried out effectively, then there is usually a high probability of success. However, planning and execution of an experiment present many opportunities for things to go wrong. This presentation illustrates several of these opportunities, based on the speaker's experiences in both academia and industry. Some of these experiences have resulted in (sometimes painful) learning opportunities. Suggestions for avoiding these problems are given.
• Friday 28th May 2004
  Speaker: Stephen Senn (University of Glasgow)
  Title: Propensity Score or Analysis of Covariance?
  Venue: Room L401, Merz Court, University of Newcastle upon Tyne
  Time: 5.30 pm
  Synopsis: An increasingly popular approach to adjusting for possible confounding in epidemiological studies and clinical trials is to stratify by the so-called 'propensity score'. I consider some disadvantages of this compared to more traditional approaches such as that of analysis of covariance.

Programme 2002/2003

• Thursday 31st October 2002
  Speaker: John Marriott (Nottingham Trent University )
  Title: A Bayesian approach to estimating smooth trends in time series
  Venue: Room CG60, University of Durham
  Time: 5.30 pm
  Synopsis: Many real time series exhibit changes in trend. However, because the observed changes are frequently not abrupt, models that specify step changes do not always adequately represent the observed behaviour. One approach to the problem has been to use smooth transition models that allow for a gradual change from one linear regime to another.

  This talk is concerned with a Bayesian approach to parameter estimation and prediction for smooth transition models.

• Thursday 21st November 2002
  Speaker: Simon Godsill (University of Cambridge)
  Title: From restoration of gramophone recordings to musical sound analysis: a collection of Bayesian methods
  Venue: Room CG60, University of Durham
  Time: 5.30 pm
  
• Thursday 12th December 2002
  Speaker: Robert Aykroyd (University of Leeds)
  Title: Bayesian modelling for industrial process tomography
  Venue: Room L401, Merz Court, University of Newcastle upon Tyne
  Time: 5.30 pm
  Synopsis: Electrical impedance tomography is a noninvasive technique used to visualise processes within physically inaccessible areas by taking measurements around their boundary. There are many potential applications to industrial processes for on-line monitoring and control of evolving processes.

  Reconstruction of images is a nonlinear inverse problem. To yield a stable solution it is necessary to make considerable use of prior information. The role of a Bayesian approach is therefore of fundamental importance, especially when coupled with MCMC sampling to provide information about solution behaviour.

• Thursday 6th February 2003
  Speaker: David Spiegelhalter (MRC Biostatistics Unit, Cambridge)
  Title: Monitoring medical outcomes: Bristol, Shipman and beyond
  Venue: Demonstration Lecture Theatre, 4th Floor Cookson Building, Medical School, University of Newcastle upon Tyne
  Time: 5.30 pm
  Synopsis: We show how statistical methods with a background in quality control can be adapted to a clinical context. These include funnel plots, risk-adjusted CUSUMS, and risk-adjusted sequential probability ratio tests. In particular we see what might have happened had such techniques been prospectively applied to the Bristol and Shipman cases.
• Thursday, 20th March 2003
  Speaker: David Howard, (Dept of Speech, University of Newcastle upon Tyne)
  Title: Statistics in practice: single cases and case series in neuropsychology
  Venue: Buttery (L401), 4th Floor Merz Court, University of Newcastle upon Tyne
  Time: 5.30 pm
  Synopsis: Cognitive neuropsychology typically uses accuracy data from single cases to investigate cognitive functioning. I will briefly discuss the reasons behind this approach and describe current statistical practice and malpractice in the field and some of the reasons behind this. Then I will consider some problems where ready made statistical solutions do not exist, and suggest, for discussion, some ways in which these problems may be addressed.
• Thursday 8th May 2003
  Speaker: Ernst Wit (University of Glasgow )
  Title: Can we make statistics count in bioinformatics?
  Venue: Room CG60, University of Durham
  Time: 5.30 pm
  Synopsis: Statistics should be the science par excellence to be leading bioinformatical endeavours. The appreciation of uncertainty and variation as a general feature of bioinformatical data typifies the enormous contribution it can make. In particular, principles of sound design are required to get workable data; exploratory methods can be used for assessing data quality and for getting preliminary results; formal high-dimensional methods coupled with statistical awareness of variation yield estimates and predictions together with measures of reliability. In this talk, I'll explore the current practice of bioinformatics and the role of statistics within it. I'll present examples of what statistics can contribute and what it has to offer for the future.

Programme 2001/2002

• Thursday 15th November 2001
  Speaker: Deborah Ashby (Queen Mary and Westfield College, London)
  Title: Evidence-based medicine as Bayesian decision-making
  Venue: Women's Club Room, Senior Common Room, University of Newcastle upon Tyne.
  Time: 5.30 pm
  Synopsis: Evidence-based medicine requires an integrated assessment of available evidence, with its associated uncertainty. There is also an emphasis on decision-making, demanding consideration of values and costs (utilities) associated with potential outcomes. We argue that the natural statistical framework is Bayesian decision-making, and propose a practical agenda for further development.
• Tuesday 11th December, 2001
  Speaker: Darren Wilkinson (University of Newcastle upon Tyne)
  Title: Stochastic modelling and inference for genetic regulatory networks
  Venue: Lecture Theatre 4, King George VI Building, University of Newcastle upon Tyne.
  Time: 5.30 pm
  Synopsis: It is generally acknowledged that the molecular mechanisms regulating key cellular processes such as gene expression are intrinsically stochastic. The random diffusion of cell signalling molecules and the combinatorial assembly of transcription factor complexes provide extensive opportunities for the action of chance. In recent years stochastic regulatory network models have been developed, based on discrete-event simulation techniques for generating realisations from the complex continuous-time countable-state Markov processes governing the reaction systems. These models contain many parameters with uncertain values. In addition, the latent process can only be observed partially, and at discrete time intervals. Inference for such Markov process models is an extremely challenging problem.

  This talk will describe the techniques used to model regulatory networks, and the computational tools needed for simulation and analysis. An overview will also be given of the MCMC algorithms which can in principle be used for carrying out Bayesian inference for the parameters underlying the network models, and the problems associated with applying such techniques in practice.

  The project's web page gives more details.

• Thursday 17th January, 2002
  Speaker: Chris Brunsdon (Department of Geography, University of Newcastle upon Tyne)
  Title: Using Shape Space Analysis To Explore Map Patterns
  Venue: Room Ph30, Department of Physics, University of Durham.
  Time: 5.30 pm
  Synopsis: There are a surprising number of statistical techniques that are of interest to geographers. Shape space analysis is one technique that seems to hold promise. Here the technique is applied to two problems: one related to identifying authorship and lineage of antique maps, and another considering the growth patterns of urban areas. Emphasis will be placed on graphical and exploratory methods, although some formal hypothesis testing will be presented.
• Thursday 21st February, 2002
  Speaker: Henry Wynn (University of Warwick)
  Title: Bayes nets and time series: trying to build causal diagrams in multivariate SPC
  Venue: Room G11, Percy Building, University of Newcastle upon Tyne
  Time: 5.30 pm