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WSS NEWS
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WSS NEWS
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The Statistics Department at the George Washington University will offer the following graduate, and special topics undergraduate courses during Spring 2006 (January 17 Ð May 16, 2006).
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/ for pertinent information.
For questions or further information please contact Dr. Tapan Nayak, e-mail: tapan@gwu.edu, ph: 202-994-6888 or visit www.gwu.edu/~stat/.
What is the difference between a fortune teller with a crystal ball and a forecaster with knowledge of time series techniques? Find out by learning the basic theory and application of regression, exponential smoothing, and the autoregressive integrated moving average (ARIMA) modeling and forecasting of univariate time series. Frequency-domain techniques will also be discussed, including the estimation of spectral density functions and performing tests of white noise and hidden periodicities. SAS will be used to demonstrate numerical examples. Prerequisite: Math 33, Stat 157-8 or 118.
This course is designed to introduce students to the fundamentals of the SAS system for data management, statistical analysis and report writing. Our goal is to provide a comprehensive understanding of and place emphasis on data modification, programming, file handling and macro writing. The course is divided into three unequal parts. The first part is devoted to the fundamentals of the SAS system. It provides an overview of the language, its capabilities, and weaknesses. The second part will concentrate on the Interactive Matrix Language. The third part of the course focuses on the components of the macro facility. Prerequisite: An introductory statistics course and Stat 129.
This is the second part of a two-part series in Mathematical Statistics. The objective is to familiarize students with the concepts of Mathematical Statistics at the graduate level. This course is a prerequisite for MS and Ph.D. students in Statistics and Biostatistics and Ph.D. students in Epidemiology. Graduate students from other related quantitative fields such as Economics, Finance, Engineering, etc. may also find this course very useful and are encouraged to join.
Stat 202 deals mostly with statistical inference (201 deals with probability theory). Topics to be covered include sampling distributions (including Central Limit Theorem), data reduction (including sufficiency, ancillarity and completeness), point estimation (including method of moments, maximum likelihood and Bayes estimation), properties of point estimators (including unbiasedness, minimum variance, efficiency, Cramer-Rao inequality), hypotheses testing (including likelihood ratio and Bayesian tests, Neyman Pearson Lemma, power and size of a test, p-value of a test), interval estimation (including Bayesian HPD intervals, intervals obtained through inversion of a test statistic or from a pivotal quantity) and asymptotic properties of procedures (including consistency and efficiency of estimators, large-sample confidence intervals, asymptotic distribution of likelihood ratio tests). This is roughly chapters 5-10 of the text: Statistical Inference by Casella and Berger (2nd ed.). Prerequisite: Multivariable Calculus (Math 33), Linear Algebra (Math 124) and Stat 201 or equivalent.
This course will review statistical principles of data analysis using computerized statistical analysis procedures provided by the Statistical Analysis System (SAS). Specific topics include: graphical displays (density estimation), univariate analyses, multiple regression, collinearity diagnostics, influence diagnostics, data-dependent model biases, analysis of contingency tables and categorical data, logistic regression for qualitative responses, analysis of variance and covariance, and the general linear model. Each week will present a statistical method and sample analyses presented in SAS listings. Each week a data analysis project will be assigned requesting that specific statistical analyses be performed and that the results be presented and interpreted in a typed statistical report. There will be a final exam and each student will also be required to complete an independent data analysis project. Prerequisites: 1) Stat 118, 2) either Stat 157 or 201, and 3) Stat 183 or equivalent or proficiency with SAS.
The objective of this course is to introduce the Ph.D. biostatistics students to several main topic areas of current research in biostatistics. It aims at introducing students to essential research resources and broadening the skills and knowledge of students for a career in biostatistical research.
This is a topic-based course. Materials are drawn from recent literatures. Four topics are covered: 1) Longitudinal Data Analysis - including random effects models, generalized estimating equations (GEE), estimation with missing data and EM Algorithm; 2) Analysis of Life Time Data - presenting the theoretical basis of concepts and methodologies associated with survival data and censoring, discussing counting processes and martingale methods; 3) Measurement Error in Nonlinear Models - introducing two general types: classical error models and regression calibration models, discussing other models for measurement error problem and 4) Introduction to the theory and modeling for planning early detection clinical trials.
Linear regression, nonparametric regression, smoothing techniques, additive models, regression trees, neural networks, and dimension reduction methods. Prerequisite: Stat 118; Math 33, 124, or equivalent.
This course will cover advanced topics in probability, which are very important for stochastic modeling and statistical analysis. The course is valuable to those who wish to Topics to be covered include: Conditional expectation and martingales; classes of distribution: infinitely divisible, stable; exchangeable random variables; Brownian motion and connection to empirical distribution functions: Kolmogorov-Smirnov tests, the Lilliefors test; stochastic differential equations; Brownian sheets and Markov fields (if time permits). Prerequisite: Stat 257 or an equivalent measure-theoretic introduction to probability.
This is the second part (along with Stat 263) of a two-semester sequence in advanced statistical theory. The course covers asymptotic theory, hypothesis testing, and confidence regions. Useful asymptotic theory for estimation and hypothesis testing is covered. In addition, one learns the theoretical foundation for the construction of UMP tests and UMP among unbiased (UMPU) tests, in the exponential family, and in particular, the normal family and the concepts of similarity and Neyman structure; confidence sets, uniformly most accurate (UMA) confidence sets and UMA unbiased confidence sets. Prerequisite: Stat 257, and 263.
Axiomatic underpinnings of Bayesian statistics, including subjective probability, belief, utility, decision and games, likelihood principle, and stopping rules. Examples from legal, forensic, biological, and engineering sciences. Students are expected to have a background in computer science, economics, mathematics, or operations research. Prerequisite: Stat 201-2.
This is the second part of a two-semester sequence on survey sampling. This course will introduce the following areas: sampling and subsampling of clusters; multistage sampling; double sampling; repetitive surveys; errors of response and nonresponse and some ways of dealing with them, and; small-area estimation. The course will cover both theory and applications. Prerequisites: Statistics 287, or equivalent.
Since the Chicago Board of option exchange opened in 1973, the options market has grown enormously. A wide variety of novel mathematical tools have been developed and employed to model and predict option prices. The 1997 Nobel Prize in Economics was awarded to Scholes and Merton for their derivation of what is now commonly known as the Black and Scholes formula. Stochastic calculus proves to be a valuable tool in option and derivatives pricing, bond pricing, and pricing of other securities. This course will present fundamental concepts and methods in stochastic calculus techniques. Specific topics will include: binomial representation theorem, ItoÕs lemma, martingale representation theorem, Black and Scholes formula, HJM interest rate model, bond pricing, and derivatives pricing. The techniques of stochastic calculus, to be discussed in this course, are useful not only in Finance, but also in other areas such as control and filtering theory, queuing and communication networks, and stochastic models in neurosciences. Prerequisites: Probability theory at the level of Stat 201, and multivariable calculus (Math 33 or equivalent).
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Return to WSS Newsletter - December, 2005 |
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First posted November 28, 2005 |
http://web.cos.gmu.edu/~wss/gwu_spring06.shtml |