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WSS NEWS
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Contents:
On Tuesday, June 7, the Washington Statistical Society (WSS) held the judging of its annual Curtis Jacobs Memorial Prize for Washington, D.C. area high school and middle school participants. There were total 69 entries. Judges selected one winner at the end. The prize of the final winner included a $200 saving bond, a plaque and a certificate to the winning team, and a one-year free American Statistical Association membership to the school. The winner and the teacher were also invited to the WSS annual dinner. Additional to the final winners, judges also selected 3 honorable mentions, who received a certificate each.
Winner
Mike Hulse, What Color Car Appeals to Most Americans
Advisor: Marvana Bennett, Oakton High School
Honorable Mentions (all from Oakton High School)
Annie Brown, New Study Confirms Consumers Declining Commitment to Fast Food Chains
Jimmy Flynn, The Call for Change
The winner team from middle school is vacant this year.
The Curtis Jacob Award committee would like to thank the following people who volunteered to judge the entries. Their help are greatly appreciated! They are:
Nancy Berman, Statistical Research Associates
Brigid Brett-Esborn, QUEST, Ernst & Young LLP
Nancy Clusen, Mathematica Policy Research
Michael P. Cohen, Department of Transportation
Amy Luo,QUEST, Ernst & Young LLP
The 2005 Federal Committee on Statistical Methodology (FCSM) Research Conference will be held November 14-16, 2005 at the Sheraton Crystal City Hotel, Arlington, Virginia. The Conference provides a forum for experts from around the world to discuss and exchange current research and methodological topics relevant to Federal government statistical programs. Each day of the conference will offer papers on a wide range of topics including the use of advanced technologies for survey design and data collection, processing and dissemination, data mining, data warehousing and metadata, treatment of missing data, improving coverage and response rates, confidentiality and disclosure issues, record linkage, sample design and estimation, cognitive research and usability testing, and data quality. Technical demonstrations on a variety of applications will run concurrently on the second day of the conference. Applications include systems for data warehousing, data analysis, clerical review, and data dissemination. Sessions feature papers and demonstrations by government, private sector, and academic researchers from six countries. In the opening plenary session Miron Straf of the National Academies of Science will discuss "A National Statistical System in a Rapidly Changing World." All paper sessions will include an open discussion and some sessions will include a formal discussion.
Conference Fee: Registration is $150.
For a copy of the advance program and registration information please refer to http://www.fcsm.gov/events/#upcoming.
Cosponsored by: American Statistical Association, ASA Section on Survey Research Methods, ASA Section on Government Statistics, International Association of Survey Statisticians, and Statistical Society of Canada
We invite you to submit proposals for invited paper sessions for presentations to the third International Conference on Establishment Surveys (ICES-III) by December 1, 2005. The ICES-III Program Committee will review the proposals and notify session organizers by the end of February, 2006 if their proposal has been accepted or not. The deadline for contributed papers (including topic contributed sessions) will be June 15, 2006.
Details on how to submit the proposal can be found at:
http://www.amstat.org/meetings/ices/2007/index.cfm
Invited sessions will have four slots of twenty five minutes each. These will be used for three 25-minute presentations, followed by a 15-minute formal discussion, and a ten-minute floor discussion.
The first International Conference on Establishment Surveys (ICES-I) in 1993 convened more than 400 experts in the area of surveys of businesses, farms and institutions. This conference set the stage by formally documenting the state of the art in 1993. In 2000 a second conference took a forward look at methods for surveying businesses, farms and institutions.
The Conference will also include:
Montreal is a city rich in history, art, culture, and learning and sports. It combines the smarts of North America with the laid back insouciance of Europe, high tech and high style. The city's architecture too is an artful mix of old and new, with graceful historic structures lovingly preserved and merged with the cool lines of the 21st century. The visual arts flourish here at the Montreal Museum of Fine Arts, the Musee D'art Contemporain, and hundreds of top-notch local galleries.
All Conference activities will be held at the Hyatt Regency Montreal. The Hyatt Regency Montreal web site is http://montreal.hyatt.com/. (Hotel reservations will be open on March 1, 2007.)
Attendees from outside Canada, and this could include United States passport holders, must ensure they have the proper travel documents e.g., passport, visa to enter and leave Canada.
Further Conference information can be accessed on the ICES-III Web link provided above.
The citation programs are built around the JPSM short courses. The JPSM Citation in Introductory Survey Methodology is designed to provide the working professional and interested students with state-of-the-art knowledge about current principles and practices for conducting complex surveys combined with practical skills of day-to-day utility. The JPSM Citation in Introductory Economic Measurement is designed for professional staff requiring a grounding in the principles and practices of economic measurement. Completion of the citation programs involves taking a semester-length JPSM credit-bearing course and eight JPSM short courses, of which four are specified core courses. For information on the Certificate and Citation Programs visit the website at http://www.jpsm.org or call 301-314-7911.
For information on JPSM short courses contact JPSM at 301-314-7911; fax 301-314-7912; or e-mail jpsm@survey.umd.edu or visit the JPSM website at http://www.jpsm.org.
Primary funding for JPSM is provided by agencies of the Federal Statistical System
The Statistics Department at The George Washington University will offer the following Graduate Courses in Fall 2005 (August 31 – December 22, 2005). Enhance your statistical analysis skills by taking one or more of these courses. Registering as a non-degree student is easy - please visit www.gwu.edu/~regweb/ and click on 'Non-Degree Registration Information' for all relevant information.
For questions or further information please contact Dr. Tapan Nayak, e-mail: tapan@gwu.edu, ph: 202-994-6888.
his is the first part of a two-part graduate level series in Mathematical Statistics. The objective of the course is to introduce students to the concepts of probability that are useful for understanding statistical theory (the course continues on to Stat 202 in Spring, which deals with the theory of statistical inference). Topics to be covered in Stat 201 include basics of probability theory (including conditional probability, Bayes theorem, random variables, density and mass functions), univariate transformations, expected values, moment generating functions, common probability distributions (including binomial, normal, uniform), multivariate distributions and transformations, covariance, inequalities and sampling distributions. This is roughly chapters 1 through 5 of the text: Statistical Inference (2nd Ed.) by Casella, G. and Berger, R. L.; Duxbury Press, CA.
This course is required for MS and Ph.D. students in Statistics, and Biostatistics, and Ph.D. students in Epidemiology. Students from other quantitative fields such as Economics, Finance, Engineering etc. would also find the course very useful and are encouraged to join.
Prerequisites: Multivariable Calculus (Math 33), and Linear Algebra (Math 124) or equivalent.
Data arising from both experimental and observational studies and in a range of applications e.g. biomedical, pharmaceutical, social science, business, reliability etc. can be typically analyzed using linear models. Applied Linear Models is an applied course aiming to provide the methodological background and computational tools for data analysis. Topics covered: Definition, fitting, inference, interpretation of results, meaning of regression coefficients, lack of fit, multicollinearity, ridge regression, principal components regression, variable selection, diagnostics, transformations, influential observations, robust procedures, ANOVA, randomized block and factorial designs. Generalized Linear Models: Binary and binomial response data, logistic regression.
Textbook: Neter, Kutner, Nachtsheim and Wasserman (1996), Applied Linear Statistical Models.
Computing: The statistical software package R will be used. R is free with Windows, Macintosh and Unix versions. Prior experience with the software is not necessary.
Prerequisites: Multivariable Calculus (Math 33), and Linear Algebra (Math 124) or equivalent.
This course will discuss parametric and nonparametric methods for the analyses of events observed in time (survival data). Topics include: survival distributions, Kaplan-Meier estimate of survival functions, Greenwood's formula, Mantel-Haenszel test, logrank and generalized logrank tests, Cox proportional hazards model, parametric regression models, and power and sample size calculations for survival analysis.
Prerequisite: Stat 201-2 or permission of instructor.
The purpose of this course is to provide a broad overview of the statistical procedures for analyzing categorical data. We will begin with analysis of contingency tables. We will talk about traditional methods for two-dimensional tables and then generalize to multidimensional tables. Theoretical bases underlying the analysis of categorical data will be covered. Different topics will include measures and tests of association; Cochran-Mantel-Haenszel procedure; weighted least squares and maximum likelihood estimators in generalized linear models; estimating equations; logistic regression; loglinear models. Computer applications (using SAS) will be considered. Prerequisite: Stat 201-2.
Text: Categorical Data Analysis, 2nd Edition, by Alan Agresti, Wiley.
This course will discuss rigorous modern measure-theoretic probability. No prior knowledge of measure theory is assumed; the necessary concepts will be developed as necessary. Topics to be covered include: Sigma fields and Probability measures, Probability Axioms, Lebesgue integration and expectation, Measure-theoretic independence, Borel-Cantelli Lemmas, Modes of probabilistic convergence, Weak and strong laws of large numbers, and Central limit theorems.
Students wishing to move on to the next level of sophistication and mathematical maturity needed for study in fields such as stochastic processes, statistics or advanced applications will find this course useful. Prerequisite: Stat-201 (MS level course in probability).
Textbooks: Karr,A. (1993). Probability. Springer, New York.
Supplemental Texts:
Chung, K. (1974). A Course in Probability Theory. Academic Press, Orlando.
Billingsley, P. (1990). Probability and Measure, 2nd Edition. Wiley, New York
This is an advanced course on principles and theory of statistical inference. Topics include: sufficiency, ancillarity, completeness, unbiased estimation, Cramer-Rao inequality, Bayesian estimation, admisibility, hypotheses testing.
Prerequisite: Stat 201-2 or permission of instructor.
This course will present mathematical theory of some statistical methods for analyzing multivariate data. Topics to be covered include: characterizations and properties of multivariate normal distribution, multiple correlation, partial correlation, estimation of the mean vector and the covariance matrix, Wishart distribution, Hotelling's T2 distribution and its applications in hypotheses testing, discrimination and classification, and multivariate analysis of variance.
Prerequisite: Stat 201-2.
The course will start with an overview of probability theory needed for an appreciation of the underlying models and methods. It will then start with an introduction of the Bernoulli process (the mother of all processes) and in the sequel introduce will introduce notions as independent increments, the Markov property, the limit theorems, stopping rules and martingales. This in turn will be followed by Markov Chains, semi-Markov processes, renewal processes, Gaussian processes, Brownian motion, and the Brownian Bridge. Applications to problems in queueing, reliability, inventory, and finance will be mentioned, and if time permits toipics such as Markov additive processes and non-Gaussian processes such as the gamma and the beta will be covered.
Stochastic processes have turned out to play a fundamental role in many disciplines, to include biology, economics, engineering, finance, operations research, and physics, to name a few. They form the building blocks of models and provide tools for the analyses of data in the context of statistical inference. Thus their appeal is universal.
Prerequisities: The course will emphasize concepts over techniques. An introductory course in probability and some mathematical maturity (Stat 201-2) is all that is needed.
Course Material: Lecture notes and topics from several books will be assembled.
The main objectives of the course are to provide a rigorous treatment of sampling theory and its applications.With this background the student can modify the existing theory, develop new theory, and better understand its applications. Graduate students from quantitative fields such as Statistics, Mathematics, Economics, Finance and Engineering as well as professionals working in government and private-sector companies, with an interest in survey sampling will benefit from this course. The prerequisites for the class are Statistics 91 (Principles of Statistical Methods) or equivalent and Math 32 (Single-Variable Calculus) or equivalent.
This course will introduce the following topics: simple random sampling with and without replacement, systematic sampling, unequal probability sampling with and without replacement, ratio estimation, difference estimation and regression estimation.
The objective of this course is to discuss statistical methods for bioinformatics. Microarrays have been widely used in biological and medical studies to identify disease related genes and to understand gene regulations. Microarray data analyses involve various statistical topics, such as multiples hypothesis testing and classification analyses. This seminar course will focus on the following topics: genomics and microarray basics, image processes, data transformation and normalization, differentially expressed gene identification, classification and cluster analysis, and other microarray related studies.
Prerequisite: Stat 157 & 158 or equivalent, or permission of instructor.
If you did not receive this issue electronically and you have not notified us that you wish to receive the newsletter in hardcopy, please send your preferred email address to svm@mitre.org and indicate that this is your address for the WSS newsletter. To continue to receive the newsletter in hard copy, contact the WSS secretary at courtney.nreiser@census.gov or (301) 763-4142.
Items for publication in the October issue of the WSS NEWS should be submitted no later than August 30, 2005. E-mail items to Michael Feil at michael.feil@usda.gov.
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First posted July 27, 2005 |
http://www.scs.gmu.edu/%7Ewss/wss0507.shtml |