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  • STATISTC 605: Prbablty Th I

    3.00 Credits
    University of Massachusetts Amherst

    A modern treatment of probability theory based on abstract measure and integration. Random variables, expectations, independence, laws of large numbers, central limit theorem, and general conditioning using the Radon-Nikodym theorem. Introduction to stochastic processes: martingales, Brownian motion. Prerequisite: Math 623.
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  • STATISTC 607: Math Statistics I

    3.00 Credits
    University of Massachusetts Amherst

    Probability theory, including random variables, independence, laws of large numbers, central limit theorem; statistical models; introduction to point estimation, confidence intervals, and hypothesis testing. Prerequisite: advanced calculus and linear algebra, or consent of instructor.
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  • STATISTC 608: Math Statistics II

    3.00 Credits
    University of Massachusetts Amherst

    Point and interval estimation, hypothesis testing, large sample results in estimation and testing; decision theory; Bayesian methods; analysis of discrete data. Also, topics from nonparametric methods, sequential methods, regression, analysis of variance. Prerequisite: Statistc 607 or equivalent.
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  • STATISTC 691C: S-Statistical Consulting

    1.00 Credits
    University of Massachusetts Amherst

    Students will listen to, and have a chance to participate in, weekly sessions on the experience of statistical consulting
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  • STATISTC 691P: S-Project Seminar

    3.00 Credits
    University of Massachusetts Amherst

    No course description available.
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  • STATISTC 691SG: S-Read-Stats of Graphs&Ntwrks

    1.00 Credits
    University of Massachusetts Amherst

    This is a research course in statistics of networks and graphs. Topics will be wide-ranging, including models for cross-sectional and dynamic networks, partially-observed networks with latent structures. Also included are network sampling, non-parametric statistical methods for networks, and data stream methods for graphs. Course discussion will highlight comparisons across frameworks for statistical analysis of networks, as well as across application areas.
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  • STATISTC 696: Independent Study

    1.00 - 6.00 Credits
    University of Massachusetts Amherst

    Contact department for description.
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  • STATISTC 697B: ST- Bayesian Statistics

    3.00 Credits
    University of Massachusetts Amherst

    This course will introduce students to Bayesian data analysis, including modeling and computation. We will begin with a description of the components of a Bayesian model and analysis(including the likelihood, prior, posterior, conjugacy, non-informativeness, credible intervals, etc.), and illustrate these objects in simple models. We will then develop Bayesian approaches to more complicated models. The course will introduce Markov chain Monte Carlo methods, and students will have the opportunity to learn to use the WinBUGS and R open source statistical packages for computation.
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  • STATISTC 697ME: ST-Measurement Error

    3.00 Credits
    University of Massachusetts Amherst

    Measurement error (errors-in-variables) which is ubiquitous, occurs when one or more of the variables of interest in a model cannot be observed exactly. In ecology this is referred to as observation error, while in economics it is called the problem of unobservable variables/use of proxy variables. This is typically due to sampling error, instrument error or a combination of the two and includes misclassification of categorical variables as a special case. Examples abound across epidemiology, ecology, economics, the physical sciences as well as most other disciplines. This course examines the impact of measurement errors on standard statistical analyses which ignore them (so-called "naive" analyses) and describes methods of correcting for measurement error using additional information or data about t he measurement error process (usually arising from replication or validation data). We examine these questions for i) misclassification in estimating one or more proportions and in two-way contingency tables; ii) measurement error in predictors and/or the response in simple and multiple linear regression as well as error in the response in estimating and comparing one or many means; iii) measurement error in nonlinear regression, including binary regression (e.g., logistic or probit) and Poisson type models. The focus is on understanding models and methods and applying them to examples from a variety of disciplines. Computing will use STATA and SAS, the two software packages for which measurement error programs have been developed. Prior experience with SAS or STATA is not required. ST797ME will have more of a theory component. Prerequisites: A background in probability and statistics at the level of STATISTC 515-516 or equivalent, some familiarity with regression analysis including experience with regression models in matrix form (e.g., ST505 or equivalent). Prior exposure to nonlinear and logistic regression is not essential, we will review the usual methods there when we get to these topics. ST797ME requires ST607-608 and ST705 (or current enrollment) or equivalent.
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  • STATISTC 697NS: ST-Network Statistics

    3.00 Credits
    University of Massachusetts Amherst

    This course focuses on modern statistical methods for analysis of network data, especially social network data. Networks are characterized by two types of units: Nodes (often representing people), and edges representing the relations between nodes. Network data are characterized by the nesting of edges between nodes, which creates interesting features for statistical dependence and sampling. In addition to statistical methods, the course will include some discussion of connections to motivating problems and underlying social science theory. It will include methods from a variety of statistical paradigms, including likelihood methods, design-based methods, and randomization methods. Most computation will be done using R statistical software. Homework assignments will include theoretical problems , computational problems, and written reading responses. Students will also complete projects of their choosing. Co-requisite: STAT 516 or 608. Recommended: One course in applied statistics (regression STAT 505/697R recommended), Experience in R.
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