A brief preamble to C.R. Rao's visit is given in Newsletter 60.

A full biography is given here.

A report on C.R. Rao's visit is given here.

C.R. Rao has been designated as a Massey University Distinguished Visitor. He will be the Keynote speaker on the first day of IWMS 2005. The first session of the Workshop commencing at 9.am, Tuesday March 29 will be an open public lecture.

C.R. Rao will also present the WCAS Workshop on 22 March 2005.

Itinerary

Monday March 07: Arrives Auckland

Tuesday March 08 - Saturday March 12: Visit Prof. Srinivasan, University of Auckland, Business School.

Monday March 14: Visit University of Otago . Give seminar: "Cross Examination of Data".

Tuesday March 15: Visit University of Canterbury. Give Seminar in afternoon. "Statistics: The science, technology and art of creating new knowledge".

Wednesday March 16: Visit University of Canterbury.

Thursday March 17: Visit Victoria University of Wellington. Give seminar: "Cross Examination of Data."

Friday March 18: Visit Massey University, Palmerston North. Give seminar: "Statistics: Reflections on the past and visions for the future."

Tuesday March 22: One-day workshop at McMeekan Centre, Ruakura, Hamilton. "Data Scrutiny and Data Mining" 4 talks.

Wednesday March 23: Visit University of Auckland, Department of Statistics. Give seminar (noon): "Statistics: Reflections on the past and visions for the future"

Thursday March 24: Visit University of Auckland.

Tuesday March 29, 9:30 am: Public lecture and IWMS Keynote talk. "Statistical proofs of matrix theorems."

Wednesday Mar 30 - Thursday March 31: IWMS, including a technical talk, "Anti eigen and singular values"

Friday April 01: Leave NZ.

Contacts

Auckland (Business) : Anath Srinivasan a.srinivasan@auckland.ac.nz
Otago : Richard Barker rbarker@maths.otago.ac.nz
Canterbury : Easaw Chacko E.Chacko@math.canterbury.ac.nz
Victoria : Estate Khmaladze Estate.Khmaladze@mcs.vuw.ac.nz
Massey : Ganes Ganesalingam s.ganesalingam@massey.ac.nz
Hamilton : Nye John nye@stats.waikato.ac.nz
Auckland (Stats) : Chris Wild c.wild@auckland.ac.nz
IWMS : Jeff Hunter j.hunter@massey.ac.nz

Talk Abstracts

Cross Examination of Data

Abstract: Data obtained from historical records, designed experiments and sample surveys are not usually in a form where routine statistical methods can be employed and inferences drawn. There may be recording errors and missing observations. The data may be faked and contaminated with irrelevant data. Usually the stochastic model generating the data, essential for data analysis, is not known. The actual procedure planned for the collection of data might not have been strictly followed. Inferential analysis of data without examining these issues might lead to wrong conclusions.

The first task of a statistician is what R.A. Fisher emphasized to cross examine the data (CED), which is to look for deficiencies in data of the type mentioned above. Some questions could be answered by questioning those who collected the data, but statisticians must have the appropriate tools to elicit the answers from the data itself. This process is described by Tukey as exploratory data analysis (EDA), and by Mahalanobis as scrutiny of data (SOD). To some extent such preliminary analysis of data is an art, but much of it could be codified.


Statistics: The science, technology and art of creating new knowledge

Abstract: Practice of statistics today extends to the whole gamut of natural and social sciences, engineering and technology, management and economic affairs, as well as arts and literature. Statistics is being applied virtually to every field to make new discoveries and breakthroughs.

There are different concepts of knowledge: true knowledge as conceived by philosophers, mathematical knowledge deduced from given axioms, scientific knowledge as embodied in scientific theories and empirical knowledge with a specified amount of uncertainty inferred from observed data. It is the last type of knowledge which enables us to take optimal decisions if an action is necessary.

Some examples of questions that have been resolved by statistics will be given. Who wrote the poem discovered in a library without any record of authorship, Shakespeare or a contemporary poet. Did Shakespeare have ghost writers? Is the expression of a gene the same in a normal person and a cancer patient? Are goods produced by a machine according to specification? Is the second born child more intelligent than the first?

Statistics: Reflections on the past and visions for the future

Abstract: Statistics is not a basic discipline like mathematics, physics, chemistry or biology each of which has a subject matter of its own on which new knowledge is built. Statistics is more a method of solving problems and creating new knowledge in other areas. Statistics is used in diverse fields such as scientific research, legal practice, medical diagnosis, economic development and optimum decision making at individual and institutional levels.

What is the future of statistics in the 21st century which is dominated by information technology encompassing the whole of communications, interaction with intelligent systems, massive data bases, and complex information processing networks? The current statistical methodology based on probabilistic models applied on small data sets appears to be inadequate to solve new problems arising in emerging areas of science, technology and policy making. Ad hoc methods are being put forward under the title Data Mining by computer scientists and engineers to meet the demands of customers. The talk will focus on a critical review of current methods of statistics and future developments based on large data sets and enormous computing power and efficient optimization techniques.


Statistical proofs of matrix theorems

Abstract: Matrix algebra is extensively used in the study of linear models, multivariate analysis and optimization problems. It is interesting to note that the matrix results needed to prove statistical propositions can themselves be deduced using some statistical results which can be derived without using matrix algebra. The results are based on Fisher information and its properties which can be established without using matrix results.

For further information concerning Professor Rao's visit contact:

Jeffrey J Hunter, Professor of Statistics
Institute of Information and Mathematical Sciences
Massey University, Albany Campus
Private Bag 102 904, North Shore Mail Centre
Auckland, 1330, NEW ZEALAND

Phone: +64 9 414 0800 Ext 41037
Fax: +64 9 441 8178
Web page: http://www.massey.ac.nz/~jhunter/
email: j.hunter@massey.ac.nz