Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
The Statistics Department at George Washington University will offer the following Graduate Course in Spring 2003 (January 13 – May 13, 2003) Enhance your statistical analysis skills by taking one or more of these courses. Registering as a non-degree student is easy – visit www.gwu.edu/~regweb/ for detailed instructions. For questions or further information please contact Dr. Tapan Nayak, e-mail: [email protected], ph: 202-994-6888. Stat 202: Mathematical Statistics. Thursdays, 6:10pm-8:40pm. Instructor: Dr. Kaushik Ghosh 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. The course is required for MS and Ph.D. students in Statistics, and Biostatistics, and Ph.D. students in Epidemiology. Graduate students from other quantitative fields such as Economics, Finance, and Engineering would also find this course useful. Prerequisite: Multivariable Calculus (Math 33), Linear Algebra (Math 124), and Stat 201 or equivalent. Stat 202 deals mostly with the statistical inference (201 deals with probability theory). Topics to be covered include sampling distributions, Central Limit Theorem, data reduction principles (sufficiency, ancillarity, and completeness), point estimation (method of moments, maximum likelihood, and Bayes estimation), properties of point estimators (unbiasedness, minimum variance, efficiency, Cramer-Rao inequality), hypotheses testing (likelihood ratio and Bayesian tests, Neyman Pearson Lemma, power and size of a test, p-value), interval estimation (Bayesian HPD intervals, intervals obtained through inversion of a test statistic or from a pivotal quantity) and asymptotic properties of procedures (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.). Stat 210: Data Analysis. Mondays, 6:10pm-8:40pm. Instructor: Dr. John Lachin Review of statistical principles of data analysis, using computerized statistical analysis procedures. Multiple regression and the general linear model, analysis of contingency tables and categorical data, logistic regression for qualitative responses, analysis of variance and covariance. Statistical analysis and interpretation of results. Complete syllabus at http://www.bsc.gwu.edu/~jml/class/stat210/syllab00.pdf. Prerequisites: Stat 118, Stat 157 or 201, and Stat 183 or equivalent. Stat 213: Intermediate Probability & Stochastic Processes. Wed., 6:10-8:40 pm. Instructor: Dr. Zhaohai Li Topics include: Random variables and their distribution; Conditional distribution and conditional expectation; Convergence of random variables: almost surely, in probability, in rth mean, in distribution; Probability inequalities: Markov inequality, CauchySchwartz inequality, Jensen's inequality; Branching process, Poisson process, Brownian motion, martingale theory. Prerequisites: Stat 201-2 or equivalent. Textbook: Probability and Random Processes by G. R. Grimmett and D. R. Strizaker. Stat 216: Applied Multivariate Analysis 2. Wednesdays, 6:10pm-8:40pm. Instructor: Dr. Reza Modarres This is the second part of a two-semester sequence in Applied Multivariate Analysis. The course is designed to introduce students to statistical analyses of several variables, most likely dependent, following a joint normal distribution. Topics include, comparisons of several population means, multivariate linear regression models, principal components, factor analysis, inference for structured covariance matrices, canonical correlations, discrimination and classification, clustering and distance methods. Additional topics from the literature will also be covered. There will be many applications of these multivariate techniques to analyses of data from the behavioral, social, medical, and physical sciences. The computational aspects will include use of matrix algebra tools (SAS/IML). Prerequisites: a course in matrix algebra (Math 124) and mathematical statistics (Stat 157-8). Textbook: Applied Multivariate Analysis, 5th Ed., by R.A. Johnson and D.W. Wichern. Statistics 221: Design of Experiments for Behavioral Sciences. Thursdays, 6:10pm8:40pm. Instructor: Dr. Curtis Tatsuoka The main objectives of this course are to give students a thorough understanding of ANOVA, and to discuss in some detail the topic of experimental design. ANOVA and methods for experimental design involve some of the most important and useful concepts that are used in statistical practice. While the course is not theoretically-oriented, the class will give a thorough presentation of ideas and formulas. SAS programming will be required, as well as a project (to be proposed by the student) that requires analysis of actual data or the development of an experimental design. Data from dissertation projects or from work are encouraged. This class should be beneficial not only to students in the behavioral sciences, but in fields such as medicine, business, and engineering. Having a background in regression and SAS programming is helpful but not essential. Topics include multifactor ANOVA, simultaneous inference, diagnostics and remedial measures for violations of assumptions in ANOVA, random and mixed effects models, analysis of covariance, randomized block design, nested designs, repeated measure designs, Latin square designs, fractional factorial designs, response surface methodology. The text is “Applied Linear Statistical Models” by Neter et. al., 4th edition, McGraw-Hill. Stat 223: Bayesian Statistics: Theory and Applications. Thurs., 6:10pm-8:40pm. Instructor: Dr. Sudip Bose This course will provide an overview of Bayesian theory, methods, and applications, starting with a discussion of why one would use Bayesian methods. Topics include foundational issues -- the likelihood principle and the Bayesian approach, conjugate priors and non-informative priors, Bayesian estimation, Bayesian testing of hypotheses and the Bayes factor, Bayesian linear models, robustness of Bayesian analyses, and computational issues. Prerequisite: Stat 201-202. Stat 258: Distribution Theory. Mondays, 6:10pm-8:40pm. Instructor: Dr. Joseph L. Gastwirth The course builds a bridge between probability theory and its application in statistics by providing the student with a repertoire of techniques of distribution theory that receive wide applications in statistics. The course is beneficial to students wishing to move on to the next level of sophistication and mathematical maturity needed for advanced study in any of the fields of stochastic processes, probability theory, or statistics. Prerequisite: Master’s level background in probability and statistics. Background in measure-theoretic probability is helpful but not required. Topics include: moments and cumulants; characteristic and moment generating functions; specialized probability inequalities, the empirical distribution Lorenz curve; asymptotic theory – the delta method, extreme value theory, large deviations; order statistics and spacing. Textbooks: Stuart, A. and Ord, K. (1987). Advanced Theory of Statistics, Vol. I Distribution Theory, Oxford University Press, and David, H. (1981). Order Statistics. Reference texts: Sarfling, R.J. (1980). Approximation Theorems of Mathematical Statistics, Wiley, New York. Linear Statistical Inference and Its Applications; Wilks, S. (1962). Mathematical Statistics. Princeton University Press, Princeton, New Jersey; Arnold B., Balakrishnan, N., and Nagarajah, H. (1992). A First Course in Order Statistics. Wiley, New York. Stat 264: Advanced Statistical Theory 2. Tuesdays, 6:10pm-8:40pm. Instructor: Dr. Subrata Kundu 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. Stat 281: Advanced Time Series Analysis. Wednesdays, 6:10-8:40pm. Instructor: Dr. Nozer D. Singpurwalla An introduction to the paradigms for forecasting using regression and time series analyses. The role of conditional expectation and inverse probability as a foundation for the structure of regression and time series models. Time series as realizations of stochastic processes such as the autoregressive and the moving average. Techniques of time series analysis. The structure of dynamic linear models and Kalman filter models and their role in time series analysis and forecasting. The control of filtered processes via the principle of maximization of expected utility. Examples, applications and case studies. Prerequisite: Math 33, Stat 201-2 or equivalent. Stat 288. Modern Theory of Survey Sampling. Thursdays, 6:10pm-8:40pm. Instructor: Dr. Promod Chandhok 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. The prerequisite for the class is Statistics 287, or equivalent. Statistics 287 introduces simple random sampling with and without replacement, systematic sampling, unequal probability sampling with and without replacement, ratio estimation, difference estimation and regression estimation. 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.