David P.M. Scollnik joined the University of Calgary in 1992 ( in no particular order: 1, 2, 3, 4, 5, 6, 7; or conduct your own web search for "review & 1992" ), and is presently an associate professor in the Department of Mathematics and Statistics where he lectures in statistics and actuarial science. He has also lectured in actuarial science at the University of Toronto. He received a combined Honours B.Sc. in pure mathematics and actuarial science from the University of Western Ontario, and both a M.Sc. and Ph.D. in statistics from the University of Toronto.
Professor Scollnik is also an Associate (A.S.A. 1988) of the professional Society of Actuaries, and his professional actuarial work experience includes service spent with Towers Perrin and Canada Life, both in Toronto, Canada. He is a member of the American Risk and Insurance Association, the American Statistical Association, and the Statistical Society of Canada. He is also an academic correspondent with the Casualty Actuarial Society. He used to be a member of the Institute of Mathematical Statistics until his membership there expired and the IMS neglected to remind him to renew :-(
Professor Scollnik's present day research ( 1, 2 ) is concentrated in the areas of actuarial science, Bayesian statistics, Markov chain Monte Carlo and related computational techniques, and also with their interplay. Actuarial science is concerned with the study of financial, business, and societal problems involving uncertain future events. This aspect of uncertainty is usually represented using statistical models. The Bayesian statistical method treats all unknown parameters appearing in these models as random variables and derives their distribution conditional upon the known information. The Bayesian paradigm may be the most natural and convenient one to adopt for the implementation and analysis of many models arising in actuarial science, insurance, and risk management. It is already the case that statistical methods with a Bayesian flavour have long been used in the insurance industry as part of the process of estimating risks and setting premiums. It should be noted that the Bayesian methodology is generally applicable to any problem in statistical modelling, not just those relating to actuarial science. However, it is true that this methodology can lead to models with a large number of variables and so these models can be very difficult to analyse analytically. Nowadays, Markov chain Monte Carlo (MCMC) computer simulation methods are routinely used in these instances. These MCMC methods are computationally intensive and often require large computing resources.
His recent publications have appeared, or are scheduled to appear, in a variety of journals including ASTIN Bulletin, Biometrics, Communications in Statistics: Theory and Method, Communications in Statistics: Simulation and Computation, Actuarial Research Clearing House, Insurance: Mathematics and Economics, Proceedings of the Casualty Actuarial Society, Transactions of the Society of Actuaries.
His e-mail ( facetiously speaking, talk about being anal retentive :-] ) address is email@example.com or firstname.lastname@example.org, and his WWW home page is located at http://balducci.math.ucalgary.ca/scully.html. By phone, he may be reached through the department office at (403) 220 5202 or (403) 220 7677.
accesses since October 1, 1998.