| source Harvard (X) |
level |
department Biostatistics (X) |
Computer arithmetic, matrix algebra, numerical optimization with application to MLEs and GEEs, spline smoothing, numerical integration, random number generation, simulation methods, Gibbs sampling, bootstrap methods, missing data and EM, imputation, and data augmentation algorithms.
Score: 10.273367 Details | Listing | Web page
This survey course, intended for a wide audience, will provide an introduction to analytic techniques for modern genomics and genetics. Topics include genome sequencing, DNA microarrays, proteomics, genetic epidemiology and gene mapping for complex disease.
Score: 10.273367 Details | Listing | Web page
Discusses the theoretical basis of concepts and methodologies associated with survival data and censoring, nonparametric tests, and competing risk models. Much of the theory is developed using counting processes and martingale methods.
Score: 10.273367 Details | Listing | Web page
The multivariate normal distribution, Hotelling's T2, MANOVA, repeated measures, the multivariate linear model, random effects and growth curve models, generalized estimating equations, multivariate categorical outcomes, missing data, computational issues for traditional and new methodologies.
Score: 10.273367 Details | Listing | Web page
General principles of the Bayesian approach, prior distributions, hierarchical models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trials, survival analysis.
Score: 10.273367 Details | Listing | Web page
Introduction to statistical methods for biological problems including microarray analysis, motif finding, CHIP-chip data, and gene regulatory network. Topics include multiple hypothesis testing, clustering and classification, variable selection, hidden Markov models, and Bayesian networks.
Score: 10.273367 Details | Listing | Web page
Sample size considerations, basic principles of experimental design (randomization, replication, and balance), block designs, factorial experiments, response surface modeling, optimal design, clinical trials, adaptive, and sequential designs.
Score: 10.273367 Details | Listing | Web page
Survey course intended for a wide audience and will provide an introduction to genomics and genetics-inspired techniques and tools for their analysis, including genome sequencing, DNA microarrays, proteomics, and high density genetic screens.
Score: 10.273367 Details | Listing | Web page
Introductory course in the analysis of Gaussian and categorical data. The general linear regression model, ANOVA, robust alternatives based on permutations, model building, resampling methods (bootstrap and jackknife), contingency tables, exact methods, logistic regression.
Score: 10.273367 Details | Listing | Web page
Intermediate course in the analysis of Gaussian, categorical, and survival data. The generalized linear model, Poisson regression, random effects and mixed models, comparing survival distributions, proportional hazards regression, splines and smoothing, the generalized additive model.
Score: 10.273367 Details | Listing | Web page
Statistical computing environments under windows and Linux systems. Taught in a computing lab, the course consists of lectures, demonstrations and hands-on exercises. Example topics include R, SAS, LaTeX, Python, and online resources.
Score: 10.273367 Details | Listing | Web page
This course focuses on selected advanced topics in design, analysis, and interpretation of clinical trials, including study design; choice of endpoints (including surrogate endpoints); interim analyses and group sequential methods; subgroup analyses; and meta-analyses.
Score: 10.273367 Details | Listing | Web page
Axiomatic foundations of probability, independence, conditional probability, joint distributions, transformations, moment generating functions, characteristic functions, moment inequalities, sampling distributions, modes of convergence and their interrelationships, laws of large numbers, central limit theorem, and stochastic processes.
Score: 10.273367 Details | Listing | Web page
Basic set theory, measure theory, Riemann-Stieltjes and Lebesgue integration, conditional probability, conditional expectation (projection), martingales, Radon-Nikodym derivative, product measure and Fubini's Theorem, limit theorems on sequences of random variables, stochastic processes, weak convergence.
Score: 10.273367 Details | Listing | Web page
An advanced course in linear models - regression and analysis of variance. Estimation (maximum likelihood and least squares) and inference (confidence intervals, hypothesis testing, analysis of residuals) are presented from a theoretical and data analysis perspective.
Score: 10.273367 Details | Listing | Web page
For doctoral candidates who have passed their written qualifying examination and who are undertaking advanced work along the lines of fundamental or applied dissertation research in the department.
Score: 10.273367 Details | Listing | Web page
For doctoral candidates who have passed their written qualifying examination and who are undertaking advanced work along the lines of fundamental or applied dissertation research in the department.
Score: 10.273367 Details | Listing | Web page
Introduction to spatial statistics with application to social science and public health research. Emphasizes methods for the analysis and visualization of three basic types of spatial data: area data, point (geostatistical) data, and point processes.
Score: 10.273367 Details | Listing | Web page
Exponential families, sufficiency, ancillarity, completeness, method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, Neyman-Pearson theory, likelihood ratio, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency.
Score: 10.273367 Details | Listing | Web page
Advanced topics in statistical inference. Limit theorems, multivariate delta method, properties of maximum likelihood estimators, saddle point approximations, asymptotic relative efficiency, robust and rank-based procedures, resampling methods, nonparametric curve estimation.
Score: 10.273367 Details | Listing | Web page
Theory of directed acyclic graph models. Identifiability of causal contrasts. Theory and applications of locally semiparametric efficient doubly-robust estimation in two models for counterfactual variables: marginal structural models and structural nested models.
Score: 10.273367 Details | Listing | Web page
The aim of this course is to develop those aspects of stochastic processes that are relevant for modeling important problems in public health. Topics include Markov processes, epidemic models, and inference associated with these models.
Score: 10.273367 Details | Listing | Web page