Queen Mary and  Westfield College
		   School of Mathematical Sciences
						       
		
	     STATISTICS SEMINAR:  DESIGN OF EXPERIMENTS

			    All are welcome


The talks are held at 16.30, all in the Mathematics Seminar Room (103)
on Level 1, Mathematics Building, Queen Mary and Westfield College.

Tea and coffee are available in the Mathematics Common Room (102) 
from 15.00.

The nearest underground station is Stepney Green. 
Turn left at the exit and walk 400 yards.


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DATE		SPEAKER				TITLE	
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21 Oct 1999	Eva Riccomagno		An application of Groebner
		EURANDOM 			bases in experimental design.
		The Netherlands		Algebraic Statistics.

4 Nov 1999	Paul Nelson			TBA
		SmithKline Beecham	

18 Nov 1999	Barbara Bogacka		Non-Linear Design Problem 
		School of Mathematical	in a Chemical Kinetic Model
		Sciences, QMW			with Non-constant Error
						Variance

2 Dec 1999	Roger Payene			A Flexible Toolkit 
		Statistics Department	for Experimental Design
		IACR-Rothamsted
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For more information ask:

Barbara Bogacka
School of Mathematical Sciences
Queen Mary and Westfield College
Mile End Road
London E1 4NS

Tel: 020 7882 5497
e-mail: [log in to unmask]
--------------------------------------------- 

The seminar information is kept on:

http://www.maths.qmw.ac.uk/~rab/seminars.html


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			   A B S T R A C T S
______________________________________________________________________	   	  
			    Eva Riccomagno

	An application of Groebner bases in experimental design. 
		         Algebraic Statistics.

In the first and main part of this talk we introduce the main
idea behind the application of Groebner basis to identification of linear
polynomial models. The focus is on the attempt to algebraise and,
to automatise to some extent, the procedure to determine an 
identifiable model given a design.  Some examples are presented where 
computational algebraic techniques are coupled with more standard 
statistical analysis. Next the possibility to extend the basic theory 
to more than one identifiable model and possible model selection 
criteria are discussed. The notion of fan of a design is introduced 
naturally. Finally a glimpse into the application in non-linear theory 
is presented. 
______________________________________________________________________


			   Barbara Bogacka 
               (joint work with Francis Wright)

	Non-Linear Design Problem in a Chemical Kinetic Model 
		   with Non-Constant Error Variance

We consider a design problem in non-linear regression, specifically in 
a chemical kinetic model. We investigate the influence of the 
dispersion structure of the random errors of observation on the design 
and its efficiency. We find that there are two kinds of designs, 
depending on the model parameters: a unique or a non-unique solution to 
the maximisation of a design optimality criterion. We investigate a wide 
range of statistical models for a simple chemical reaction and discuss 
the possible solutions to the design problem. We indicate the advantage 
of statistical approach to the determination of the kinetic parameters 
as opposed to the standard methods used in chemistry, based on the 
knowledge on the reaction rate law. We also discuss the practical 
aspects of planning an optimum experiment for the estimation of the 
kinetic parameters.
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				Roger Payne
		A Flexible Toolkit for Experimental Design

Statistical software can provide considerable help and guidance in the 
design of experiments. It is important, however, not to impose 
limitations over the range or types of experiments that are supported 
by allowing choices only from a fixed set of repertoires.

In this talk I shall describe the philosophy that lies behind the Genstat
design system. This supports a range of standard generators, 
such as design keys, Hadamard matrices and Alpha arrays. Files of 
generators are provided to cover various standard designs, and these 
are used to form interactive menus that allow users to select a design, 
to name and generate its factors, print a plan and then (where appropriate)
produce a dummy analysis of variance. 
Further facilities allow designs and data forms to be displayed, and simple
designs to be combined into more complicated arrangements. 
Algorithms are available to form generators for new designs, and these 
can be added to the design files to become an integral part of the 
system. Examples will be given to illustrate the framework and 
underlying methodology, and to show how the various facilities 
combine to provide a very flexible toolkit for design and analysis of 
experiments.
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