09:00 to 09:30 D Majumder ([Illinois at Chicago])Optimal designs for 2^k experiments with binary responseSession: Discrete ResponsesChair: Dave Woods We consider optimal designs for an experiment with k qualitative factors at 2 levels each with binary response. For local D-optimality we derive theoretical results, propose algorithms to search for optimal designs and study properties of the algorithms. The robustness of optimal designs to specification of assumed parameter values is studied. We also briefly examine Bayesian optimal designs. INI 1 09:30 to 10:00 K Russell (Charles Sturt University)D-optimal designs for multinomial experimentsSession: Discrete ResponsesChair: Dave Woods Consider a multinomial experiment where the value of a response variable falls in one of k classes. The k classes are not assumed to have a hierarchical structure. Let ij represent the probability that the ith experimental unit gives a response that falls in the jth class. By modelling ln(ij=i1) as a linear function of the values of m predictor variables, we may analyse the results of the experiment using a Generalized Linear Model. We describe the construction of D-optimal experimental designs for use in such an experiment. Difficulties in obtaining these designs will be described, together with attempts to overcome these obstacles. INI 1 10:00 to 10:30 B Torsney (University of Glasgow)Fitting Latent Variable Models for Paired Comparisons and Ranking Studies - An Application of Optimal Design TheorySession: Discrete ResponsesChair: Dave Woods In a paired comparisons experiment a subject has to indicate which of two 'treatments' Ti, Tj is preferred. We observe Oij, the frequency with which Ti is preferred to Tj.in nij comparisons. Under a class of models for such data, which include the Bradley Terry and Thurstone models, P(Ti is preferred to Tj) = F( i - j), where F(.) is a symmetric distribution function and ( i) is a treatment index. For identifiability purposes constraints must be imposed on parameters. One is to assume that ipi = 1, where pi = ln( i); an alternative is ipi = 1. Thus theorems identifying optimal design weights and algorithms for determining them carry over to the maximum likelihood estimation of these parameters. Of course these tools can also be used to determine locally optimal designs for such models. We will explore this fusion of topics, taking the opportunity to expand on the class of models, both for simple paired comparisons data and also for data consisting of orderings or rankings. In particular we will exploit multiplicative algorithms for maximum likelihood estimation. INI 1 10:30 to 11:00 Morning coffee 11:00 to 11:30 S Sahu ([Southampton])Bayesian Adaptive Design for State-space Models with CovariatesSession: Design for Observational SystemsChair: Stefanie Biedermann Modelling data that change over space and time is important in many areas, such as environmental monitoring of air and noise pollution using a sensor network over a long period of time. Often such data are collected dynamically together with the values of a variety of related variables. Due to resource limitations, an optimal choice (or design) for the locations of the sensors is important for achieving accurate predictions. This choice depends on the adopted model, that is, the spatial and temporal processes, and the dependence of the responses on relevant covariates. We investigate adaptive designs for state-space models where the selection of locations at time point $t_{n+1}$ draws on information gained from observations made at the locations sampled at preceding time points $t_1, \ldots, t_n$. A Bayesian design selection criterion is developed and its performance is evaluated using several examples. INI 1 11:30 to 12:00 B Parker ([QMUL])Design of Networked ExperimentsSession: Design for Observational SystemsChair: Stefanie Biedermann We consider experiments on a number of subjects, and examine how the links between subjects in an experiment, affect the optimal design. For example, in a marketing experiment, it is reasonable to believe that a product may be preferred more by a subject whose 'friend' also prefers that product, and we may wish to use this 'friendship' information to improve our design. We present optimal designs to measure both the direct effect and the network effect. We discuss how the structure of the network has a large influence on the optimal design, but show that even if we know many properties of the network, as represented by the eigenvalues of a graph, we cannot determine an absolute design. We present examples based on marketing experiments, and show how the results can be applied to experiments in social sciences and elsewhere. INI 1 12:00 to 12:20 Poster StormChair: Ken Russell INI 1 12:30 to 13:30 Lunch at Wolfson Court 14:30 to 16:00 Trip to Rothamsted - Tour of long-term field experimentsChair: Sue Welham 16:00 to 16:20 Trip to Rothamsted - Tea 16:20 to 16:50 R Payne Trip to Rothamsted - The Development of Statistical Design Concepts at Rothamsted Modern applied statistics began in 1919, when R.A. Fisher was appointed as the first statistician at Rothamsted. Experiments were already taking place of course, notably the Broadbalk long-term fertiliser trial of wheat at Rothamsted which had been running since 1843. However, concepts like replication, randomization, blocking and factorial structure were unknown – and it took some strong persuasion by Fisher before they became accepted. Only very limited analysis options were available too, and again some of the concepts like degrees of freedom, or methodology like maximum likelihood, proved to be very controversial. Nevertheless, by the time that Fisher left Rothamsted in 1935, the foundations had been laid for very many of the design principles and statistical methods that we now take for granted. In this talk I shall describe how some of the key ideas developed under the challenges from the biological research at Rothamsted, sketch out a few of the events and controversies, and indicate how some of the research threads have been continued by his successors. 19:30 to 22:00 Conference dinner at Christ's College