STAT910 - FORCAS & TIME SER ANALY
Fourier analysis of data, stationary time series, properties of autoregressive moving average models and estimation of their parameters, spectral analysis, forecasting. Discussion of applications to problems in economics, engineering, physical science, and life science.
Prerequisites: STAT 520 or 961 or equivalent
STAT915 - NONPARAMETRIC INFERENCE
Statistical inference when the functional form of the distribution is not specified. Nonparametric function estimation, density estimation, survival analysis, contingency tables, association, and efficiency.
Prerequisites: STAT 520 or equivalent
STAT920 - SAMPLE SURVEY METHODS
This course will cover the design and analysis of sample surveys. Topics include simple random sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias.
Prerequisites: STAT 520, 961 or 970 or permission of instructor
STAT921 - OBSERVATIONAL STUDIES
This course will cover statistical methods for the design and analysis of observational studies. Topics will include the potential outcomes framework for causal inference; randomized experiments; matching and propensity score methods for controlling confounding in observational studies; tests of hidden bias; sensitivity analysis; and instrumental variables.
Prerequisites: STAT 520, 961 or 970 or permission of instructor
STAT925 - MULTIVARIATE ANALY: THEO
This is a course that prepares PhD students in statistics for research in multivariate statistics and high dimensional statistical inference. Topics from classical multivariate statistics include the multivariate normal distribution and the Wishart distribution; estimation and hypothesis testing of mean vectors and covariance matrices; principal component analysis, canonical correlation analysis and discriminant analysis; etc. Topics from modern multivariate statistics include the Marcenko-Pastur law, the Tracy-Widom law, nonparametric estimation and hypothesis testing of high-dimensional covariance matrices, high-dimensional principal component analysis, etc.
Prerequisites: STAT 930, 970 and 972 or permission of instructor
STAT926 - MULTIVARIATE ANALY: METH
This is a course that prepares PhD students in statistics for research in multivariate statistics and data visualization. The emphasis will be on a deep conceptual understanding of multivariate methods to the point where students will propose variations and extensions to existing methods or whole new approaches to problems previously solved by classical methods. Topics include: principal component analysis, canonical correlation analysis, generalized canonical analysis; nonlinear extensions of multivariate methods based on optimal transformations of quantitative variables and optimal scaling of categorical variables; shrinkage- and sparsity-based extensions to classical methods; clustering methods of the k-means and hierarchical varieties; multidimensional scaling, graph drawing, and manifold estimation.
Prerequisites: STAT 961 or permission of instructor
STAT927 - BAYESIAN STATISTICS (Course Syllabus)
This graduate course will cover the modeling and computation required to perform advanced data analysis from the Bayesian perspective. We will cover fundamental topics in Bayesian probability modeling and implementation, including recent advances in both optimization and simulation-based estimation strategies. Key topics covered in the course include hierarchical and mixture models, Markov Chain Monte Carlo, hidden Markov and dynamic linear models, tree models, Gaussian processes and nonparametric Bayesian strategies.
Prerequisites: STAT 430 or STAT 510
STAT928 - STAT LEARNING THEORY
Statistical learning theory studies the statistical aspects of machine learning and automated reasoning, through the use of (sampled) data. In particular, the focus is on characterizing the generalization ability of learning algorithms in terms of how well they perform on "new" data when trained on some given data set. The focus of the course is on: providing the fundamental tools used in this analysis; understanding the performance of widely used learning algorithms; understanding the "art" of designing good algorithms, both in terms of statistical and computational properties. Potential topics include: empirical process theory; online learning; stochastic optimization; margin based algorithms; feature selection; concentration of measure.
Prerequisites: Probability and linear algebra.
STAT930 - PROBABILITY (Course Syllabus)
Measure theory and foundations of Probability theory. Zero-one Laws. Probability inequalities. Weak and strong laws of large numbers. Central limit theorems and the use of characteristic functions. Rates of convergence. Introduction to Martingales and random walk.
Prerequisites: STAT 430 or 510 or equivalent
STAT931 - STOCHASTIC PROCESSES
Markov chains, Markov processes, and their limit theory. Renewal theory. Martingales and optimal stopping. Stable laws and processes with independent increments. Brownian motion and the theory of weak convergence. Point processes.
Prerequisites: STAT 930
STAT955 - STOCH CAL & FIN APPL
Selected topics in the theory of probability and stochastic processes.
Prerequisites: STAT 930 or equivalent
STAT957 - SEMINAR IN DATA ANALYSIS
Survey of methods for the analysis of large unstructured data sets: detection of outliers, Winsorizing, graphical techniques, robust estimators, multivariate problems.
Prerequisites: STAT 961, 971, 972, 925, or equivalents; permission of instructor
STAT961 - STATISTICAL METHODOLOGY (Course Syllabus)
This is a course that prepares 1st year PhD students in statistics for a research career. This is not an applied statistics course. Topics covered include: linear models and their high-dimensional geometry, statistical inference illustrated with linear models, diagnostics for linear models, bootstrap and permutation inference, principal component analysis, smoothing and cross-validation.
Prerequisites: STAT 431 or 520 or equivalent; a solid course in linear algebra and a programming language
STAT962 - ADV METHODS APPLIED STAT
This course is designed for Ph.D. students in statistics and will cover various advanced methods and models that are useful in applied statistics. Topics for the course will include missing data, measurement error, nonlinear and generalized linear regression models, survival analysis, experimental design, longitudinal studies, building R packages and reproducible research.
Prerequisites: STAT 961
STAT970 - MATHEMATICAL STATISTICS (Course Syllabus)
Decision theory and statistical optimality criteria, sufficiency, point estimation and hypothesis testing methods and theory.
Prerequisites: STAT 431 or 520 or equivalent; comfort with mathematical proofs (e.g., MATH 360)
STAT971 - INTRO TO LINEAR STAT MOD (Course Syllabus)
Theory of the Gaussian Linear Model, with applications to illustrate and complement the theory. Distribution theory of standard tests and estimates in multiple regression and ANOVA models. Model selection and its consequences. Random effects, Bayes, empirical Bayes and minimax estimation for such models. Generalized (Log-linear) models for specific non-Gaussian settings.
Prerequisites: STAT 970
STAT972 - ADV TOPICS IN MATH STAT (Course Syllabus)
A continuation of STAT 970.
Prerequisites: STAT 970 and 971
STAT974 - MODERN REGRESSION (Course Syllabus)
Function estimation and data exploration using extensions of regression analysis: smoothers, semiparametric and nonparametric regression, and supervised machine learning. Conceptual foundations are addressed as well as hands-on use for data analysis.
Prerequisites: Two statistics courses at the graduate school level including a solid foundation in the generalized linear model.
STAT991 - SEM IN ADV APPL OF STAT (Course Syllabus)
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
STAT995 - DISSERTATION
STAT999 - INDEPENDENT STUDY
Prerequisites: Written permission of instructor and the department course coordinator.
The Wharton School,
University of Pennsylvania
400 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
Phone: (215) 898-8222
Fax: (215) 898-1280
- Divyansh Agarwal, MD-PhD Student (Medical Scientist Training Program)
- Cecilia Balocchi, PhD Student
- Debapratim Banerjee, PhD Student
- Junhui Cai, PhD Student
- Ran Chen, PhD Student
- Shuxiao Chen, PhD Student
- Emily Diana, PhD Student
- Sheng Gao, PhD Student
- Raiden Hasegawa, PhD Student
- Siyu Heng, PhD Student
- Mo Huang, PhD Student
- Bikram Karmakar, PhD Student
- Justin Khim, PhD Student
- Arun Kumar Kuchibhotla, PhD Student
- Shaokun Li, PhD Student
- Gemma Moran, PhD Student
- Somabha Mukherjee, PhD Student
- Seth Neel, PhD Student
- Hongming Pu, PhD Student
- Saeed Sharifi-Malvajerdi, PhD Student
- Matteo Sordello, PhD Student
- Yichen Wang, PhD Student
- Xuran Wang, PhD Student in Applied Math
- Hua Wang, PhD Student
- Hongji Wei, PhD Student
- Mateo Wirth, PhD Student
- Ruijia Wu, PhD Student
- Ruoqi Yu, PhD Student
- Linjun Zhang, PhD Student
- Bo Zhang, PhD Student
- Zilu Zhou, PhD Student in Genomics and Computational Biology