Modern Interdisciplinary University Statistics Education Proceedings of a Symposium (1994) / Chapter Skim
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Modernizing Statistics PhD Programs
Modernizing Statistics PhD Programs
Pages 31-40
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From page 31... ...
Nevertheless, if substantial cross-disciplinary training is introduced into the graduate curriculum, this Will naturally offer opportunities for advanced undergraduate student involvement, and this will certainly enrich any undergraduate statistics program. The Fufare Universif, Environment The question of modernizing statistics education cannot be addressed without first recognizing the significant forces that have taken shape in the last several years.
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From page 32... ...
It will show a whole new world of material unknown 25 years ago, using words such as "bootstrap," "Bayes-empirical Bayes," "CART," "projection pursuit," "ACE," "MARS," "Gibbs sampling," and so on, not to mention the renaissance of experimental design work spawned by new industrial statistics problems, or the tremendous growth of biostatistics both in applications and methodological advances. Statisticians are attacking problems an order of magnitude harder than those discussed 25 years ago; the wide interest in image restoration is an example.
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From page 33... ...
While the first observation should harden the resolve to find formal mechanisms to help train statistics PhD students to effectively contribute to cross-disciplinary studies, never forget that there is an ever increasing body of material that students are expected to master to be literate statisticians and effective contributors to cross-disciplinary teams. The challenge is not only how to increase cross-disciplinary training, but also to find a way to do it that does not compromise the knowledge and skills a statistician must have to effectively contribute.
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From page 34... ...
It is recognized that each student has individual strengths and weaknesses; however, every student who receives a PhD degree must have a solid mastery of basic probability and statistical theory, be knowledgeable about a wide range of statistical methods, be experienced at handling statistical applications, and be capable of computing effectively. The faculty is unified in its view that ALL students must meet the spirit of these requirements.
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From page 35... ...
For example, one pair might work with an analyst in the CMU planning office to develop insights into the undergraduate dropout rate, or why applicants decide to attend schools other than CMU.2 There is a wealth of potential projects and willing subject matter participants, because the statistics faculty have many ongoing collaborations with other researchers at CMU or at the University of Pittsburgh. This course also covers material on report writing and presentation skills.
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From page 36... ...
The first two courses in advanced statistics and advanced probability are common to most PhD programs in statistics, with perhaps the only distinction being that the inference course usually contains material on the foundations of statistics and Bayesian theory. Students must pass qualifying examinations in both subjects.
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From page 37... ...
More recently, working groups consisting of faculty and students have sprung up for short periods of time to study some specific topic such as spatial statistics, Gibbs sampling, or graphics. As one who took a large number of advanced topics courses as a graduate student, ~ have always felt uncomfortable with the lack of required traditional advanced topics courses such as non-parametric or sequential analysis.
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From page 38... ...
It was recognized that this variability had to be dramatically reduced, and it had to be done immediately, because nearly all of these students would be grading papers very shortly. In addition to having completed teacher training, by the time each of our PhD students graduates she or he will have made many in-class presentations and at least three major departmental presentations, including the advanced data analysis project, the thesis proposal, and the thesis defense, each of which is attended by the entire faculty and student body.
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From page 39... ...
Even in autonomous departments of statistics, it will take time and leadership for the departmental culture to change so that cross-disciplinary contributions are thought of as being as important as papers published in our core theoretical journals. Departments will need to develop standards for evaluating coauthored papers and papers on substantive topics published in non-statistical journals.
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From page 40... ...
Appendix Examples of Statistical Practice Projects · National Institute of Mental Health treatment of depression collaborative trial · Longitudinal patterns of psychological distress following the Three Mile Island accident · Factors related to early mortality among U.S. servicemen following deployment in Vietnam · Discounts and quality premiums for illicit drugs · Suicidal behavior in schizophrenics · Analysis of clinical trial data on the effects of behavioral and pharmacological interventions in children with attention deficit disorder Examples of Advanced Data Analysis Projects · Analysis of data from a fiberglass production facility · Detennining the sources of lead contamination in soil · Predicting enrollments at Carnegie Mellon University · Survival times in patients with recurrent depression · Analysis of oceanographic data · A Bayesian analysis of bivariate survival data from a multicenter cooperative cancer clinical trial 40
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