| source Harvard (X) |
level |
department Statistics (X) |
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Score: 8.88386 Details | Listing | Web page
An introduction to modern financial markets, and the probabilistic and statistical techniques used to navigate them. Methodology will be motivated wherever possible by real problems from the financial industry. Topics include: interest-rates, swap markets and fixed income securities; structured note construction and valuation; options markets and probabilistic approaches to valuation; electronic trading and performance evaluation. Designed for those seeking a basic understanding of the evolution of quantitative challenges on Wall Street, and the tool-kit developed to address them.
Score: 8.88386 Details | Listing | Web page
Basic Bayesian models, followed by more complicated hierarchical and mixture models with nonstandard solutions. Includes methods for monitoring adequacy of models and examining sensitivity of models.
Score: 8.88386 Details | Listing | Web page
Methods for design and analysis of sample surveys. The toolkit of sample design features and their use in optimal design strategies. Sampling weights and variance estimation methods, including resampling methods. Brief overview of nonstatistical aspects of survey methodology such as survey administration and questionnaire design and validation (quantitative and qualitative). Additional topics: calibration estimators, variance estimation for complex surveys and estimators, nonresponse, missing data, hierarchical models, and small-area estimation.
Score: 8.88386 Details | Listing | Web page
Statistical designs for efficient experimentation in physical, chemical, biological, social and management sciences and in engineering. A systematic approach to explore input-output relationships by deliberately manipulating input variables. Topics include analysis of variance, completely randomized and randomized block designs, Latin square designs, balanced incomplete block designs, factorial designs, confounding in blocks, fractional replications, orthogonal arrays, and response surface designs. Each topic is motivated by a real-life example.
Score: 8.88386 Details | Listing | Web page
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Score: 8.88386 Details | Listing | Web page
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Score: 8.88386 Details | Listing | Web page
Meets with Statistics 115, but graduate students are required to complete a research project and make a final presentation during reading period in addition to completing all work assigned for Statistics 115.
Score: 8.88386 Details | Listing | Web page
Basic problems, algorithms and data analysis approaches in computational biology. Topics include sequence alignment, genome sequencing and gene finding, gene expression microarray analysis, transcription regulation and sequence motif finding, comparative genomics, RNA/protein structure prediction, proteomics and SNP analysis. Computational algorithms covered include hidden Markov model, Gibbs sampler, clustering and classification methods.
Score: 8.88386 Details | Listing | Web page
A comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes' Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.
Score: 8.88386 Details | Listing | Web page
Similar to Statistics 100, but emphasizes applications in fields including, but not limited to, economics, health sciences and policy analysis. Topics covered: descriptive and summary statistics for both measured and counted variables; elements of experimental and survey design; probability; and statistical inference including estimation and tests of hypotheses as applied to one- and two-sample problems, multiple regression, correlation, and analysis of variance. Taught at a slightly higher level than Statistics 100 and 101.
Score: 8.88386 Details | Listing | Web page
Similar to Statistics 100, but emphasizes applications in fields including, but not limited to, economics, health sciences and policy analysis. Topics covered: descriptive and summary statistics for both measured and counted variables; elements of experimental and survey design; probability; and statistical inference including estimation and tests of hypotheses as applied to one- and two-sample problems, multiple regression, correlation, and analysis of variance. Taught at a slightly higher level than Statistics 100 and 101.
Score: 8.88386 Details | Listing | Web page
Similar to Statistics 100, but emphasizes concepts and practice of statistics used in psychology and other social and behavioral sciences. Topics covered: describing center and variability; probability and sampling distributions; estimation and hypothesis testing for comparing means and comparing proportions; contingency tables; correlation and regression; multiple regression; analysis of variance. Emphasis on translation of research questions into statistically testable hypotheses and models, and interpretation of results in context.
Score: 8.88386 Details | Listing | Web page
Similar to Statistics 100, but emphasizes concepts and practice of statistics used in psychology and other social and behavioral sciences. Topics covered: describing center and variability; probability and sampling distributions; estimation and hypothesis testing for comparing means and comparing proportions; contingency tables; correlation and regression; multiple regression; analysis of variance. Emphasis on translation of research questions into statistically testable hypotheses and models, and interpretation of results in context.
Score: 8.88386 Details | Listing | Web page
Introduction to key ideas underlying statistical and quantitative reasoning. Topics covered: methods for organizing, summarizing and displaying data; elements of sample surveys, experimental design and observational studies; methods of parameter estimation and hypothesis testing in one- and two-sample problems; regression with one or more predictors; correlation; and analysis of variance. Explores applications in a wide range of fields, including the social and political sciences, medical research, and business and economics.
Score: 8.88386 Details | Listing | Web page
Introduction to key ideas underlying statistical and quantitative reasoning. Topics covered: methods for organizing, summarizing and displaying data; elements of sample surveys, experimental design and observational studies; methods of parameter estimation and hypothesis testing in one- and two-sample problems; regression with one or more predictors; correlation; and analysis of variance. Explores applications in a wide range of fields, including the social and political sciences, medical research, and business and economics.
Score: 8.88386 Details | Listing | Web page
Meets with Statistics 170, but graduate students will be exposed to a more rigorous treatment of stochastic calculus.
Score: 8.88386 Details | Listing | Web page
Introduces stochastic analysis tools to be used as a basis for developing continuous-time asset pricing theory. Various quantitative methods widely used in the financial industry for valuing derivative products will be presented: binomial-tree valuation methods, extensions of the Black-Scholes option pricing formula, numerical techniques for solving partial differential equations, and Monte Carlo simulations.
Score: 8.88386 Details | Listing | Web page
An introductory course in stochastic processes. Topics include Markov chains, branching processes, Poisson processes, birth and death processes, Brownian motion, martingales, introduction to stochastic integrals, and their applications.
Score: 8.88386 Details | Listing | Web page
Basic concepts of statistical inference from frequentist and Bayesian perspectives. Topics include maximum likelihood methods, confidence and Bayesian interval estimation, hypothesis testing, least squares methods and categorical data analysis.
Score: 8.88386 Details | Listing | Web page
This course provides an accessible introduction to the study of matched sampling and other design techniques in any field (e.g., economics, education, epidemiology, medicine, political science, etc.) conducting empirical research to evaluate the causal effects of interventions.
Score: 8.88386 Details | Listing | Web page
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Score: 8.88386 Details | Listing | Web page
Multivariate inference and data analysis. Advanced matrix theory and distributions, including Multivariate Normal, Wishart, and multilevel models. Supervised learning: multivariate regression, classification, and discriminant analysis. Unsupervised learning: dimension reduction, principal components, clustering, and factor analysis.
Score: 8.88386 Details | Listing | Web page
Theory of multi-level parametric models, including hidden Markov models, and applications likely to include biostatistics, health services, education, and sports.
Score: 8.88386 Details | Listing | Web page
Random variables, measure, representations. Families of distributions: Multivariate Normal, conjugate, marginals, mixtures. Conditional distributions and expectation. Convergence, laws of large numbers, and central limit theorems. Markov chains and martingales.
Score: 8.88386 Details | Listing | Web page