Diversities of gifts, but the same spirit

1. Diversities of gifts, but the same spirit Peter Green RSS Presidential address 18 June 2003

2. A discipline of diversity Public life Social science Science Technology Medicine Statistics Business and industry

3. philosophical foundations mathematical theory inferential principles design data collection techniques computation modelling A discipline of diversity

4. A discipline of diversity Interaction with the rest of the world is part of the subject itself

5. shelter and nourishment for statistics • a microcosm of

6. Connection or fragmentation? What holds us together?

7. Connection or fragmentation? Statistics in Society • getting the correct denominator in workforce statistics • computing DNA match probabilities • assessing clinical effectiveness • evaluating GM crop experiments

8. Connection or fragmentation? Heterogeneity of discipline • intellectual strength • structural weakness

9. medical industrial core social official

10. medical industrial core social official

11. medical industrial core social official

12. How the discipline develops • Promoting our strengths should be a key priority for the discipline and for the Society

13. How the discipline develops – demands of applications • Public policy • evidence-based decision making • performance measurement • Legal system • scientific evaluation of evidence • Social science • respect for quantification • public archives, National Statistics

14. How the discipline develops – demands of applications • Business • data-mining • Technology • uncertainty in telecomms, images • Science • all scales: Astronomy to Genomics • quantum level?

15. How the discipline develops – opportunity of technology • Sensors and instrumentation • Data-logging capacity • Communications • Number-crunching • transforming quantity and quality of data • enabling highly computer-intensive analysis

16. How the discipline develops – theoretical innovation • Relaxation of old philosophical quarrels • Rehabilitation of Bayesian methods • Key role of conditional inference • graphical modelling • Stochastic calculus • martingales • Point processes

17. The role of statistical modelling • underpinning all parts of the discipline • the most basic tabulation or summary involves conceptualisation • what can vary? • on what scale? • depending on what?

18. The role of statistical modelling • Discipline in creation of methodology • Framework • for study of foundations • for expressing principles • for provision of computational tools • Use more to communicate ideas • & break down barriers between theory and practice?

19. Structured systems • A framework for building models, especially probabilistic models, for empirical data

21. Markov chains Spatial statistics Genetics Regression AI Statistical physics Sufficiency Covariance selection Contingency tables Graphical models

22. Structured systems Key idea - understand complex system through global model built from small pieces • comprehensible • modular • each with only a few variables

23. Modular structure Basis for • understanding the real system • capturing important characteristics statistically • defining appropriate methods • computation • inference and interpretation

24. Conditional independence • X and Z are conditionally independent given Y if, knowing Y, discovering Z tells you nothing more about X • X  Z  Y X Y Z

25. Conditional independence as seen in data…. Does survival depend on ante-natal care? .... what if you know the clinic?

26. Conditional independence survival ante clinic survivaland clinicaredependent andanteandclinicaredependent but survival and ante are CI given clinic

29. AB AO AO OO OO A natural directed graph from genetics A AB A O O Mendel

30. Model for lip cancer data regression coefficient covariate random spatial effects relative risks observed counts

31. or non- Bayesian

32. Bayesian structured modelling • ‘borrowing strength’ • automatically integrates out all sources of uncertainty • properly accounting for variability at all levels • including, in principle, uncertainty in model itself

33. Bayesian structured modelling • ‘borrowing strength’ • automatically integrates out all sources of uncertainty • … for example in forensic statistics with DNA probe data…..

34. (thanks to J Mortera)

36. Bayesian structured modelling • ‘borrowing strength’ • automatically integrates out all sources of uncertainty • … for example in modelling complex biomedical systems like ion channels…..

37. Ion channelmodel model indicator transition rates hidden state Hodgson and Green, Proc Roy Soc Lond A, 1999 binary signal levels & variances data

38. model indicator C1 C2 C3 O1 O2 transition rates hidden state binary signal levels & variances data * * * * * * * * * * *

39. Structured systems’ success stories include... • Genomics & bioinformatics • DNA & protein sequencing, gene mapping, evolutionary genetics • Spatial statistics • image analysis, geographical epidemiology • Temporal problems • longitudinal data, financial time series, signal processing

40. The methodology gap • Subgroups develop their own ideas and jargon • Weaker communication between than within • Little evidence in RSS journals • But wide use of outdated and inappropriate statistical techniques in some areas

41. The methodology gap - the pressures: • pace of working life  specialisation  quick approximations • training more focussed • both theoretical and applied

42. The methodology gap • RSS provides something for almost every specialism • but how many of us exploit that?

46. Making more of methodology • Relevance to applications is the main stimulus and justification • But, for the sake of the vigour of the subject and cross-fertilisation between applications, there is a vital role for ‘generic methodology’ • not mathematical statistics • not application-specific

47. Generic methodology • The generalised likelihood ratio test • Fisher scoring • The practice of fitting dose-response relationships by MLE all existed before….

49. Generic methodology …. but the generalised linear model framework did not just unify, • it generated new application-specific technique • it promoted good practice generally

50. A good methodology paper might cover all of ... • underlying philosophical principles • mathematical development • statistical modelling of a real process • computational implementation • data analysis • model criticism • interpretation of inference and performance