Recent Courses

  • Harvard University, GOV 52: Models

    Semester
    Spring
    Year offered
    2021
    This course covers statistical models useful for investigating political and social phenomena. We will address the theory and principles behind these methods, their applications, and their limitations. The course will be useful for those undertaking a quantitative thesis or wanting to have data science modeling skills for other applications. Specific models to be studied include the standard linear regression model in detail, and Generalized Linear Model forms such as logit, probit, log-linear (Poisson), Gamma, ordered choice, multinomial, tobit, and more. In addition Bayesian estimation, Monte Carlo simulation, multilevel, and nonparametric models will be covered.
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  • American University: Statistics 618/GOV 618 (Every FALL): Bayesian Statistics for Social and Biomedical Sciences

    Semester
    Fall
    Year offered
    2020

    Course Description
    Principles and applications of modern statistical decision theory, with a special focus on Bayesian modeling, data analysis, inference, and optimal decision making. Prior and posterior; comparison of Bayesian and frequentist approaches, including minimax decision making and elementary game theory. Bayesian estimation, hypothesis testing, credible sets, and Bayesian prediction. Introduction to Bayesian computing software and applications to diverse fields. Grading: A-F only. Prerequisite: STAT-514 or permission of instructor... 

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Data Science Institute - January 2021

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Recent Publications

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Tenure Home: School of Public Affairs, College of Arts & Sciences

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Featured Recent Paper

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Nikoleta S. Kolovos, Jeff Gill, Peter H. Michelson, Allan Doctor, Mary E. Hartman. ``Reduction in Mortality Following Pediatric Rapid Response Team Implementation.'' Pediatric Critical Care Medicine, 2018.

Paper LinkPubMed.

I am also Editor-in-Chief of Political Analysis

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Political Analysis

            APSA 2020 Report

Concordia University WSSR Workshop at SPSA

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January 6, 2021

8:00 a.m. - 11:00 a.m. (New Orleans time)           

"Generalized Linear Regression Models for Social Scientists" - Part I

12:30 p.m. - 3:30 p.m. (New Orleans time)           

"Generalized Linear Regression Models for Social Scientists" - Part II

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