Maximum Likelihood Estimation (Houston, TX)

Instructor(s):

  • Ling Zhu, University of Houston

This workshop introduces participants to a number of generalized linear models, with an emphasis on likelihood-based methods. From survey responsesto event occurrences and event counts, political and social science data are nearlyalways categorized by the limited distribution of the variables we are interested inmodeling. In this class we will explore limited-dependent variable models, and how social scientists can make use of these models to test arguments. We will tackle boththe theoretical under-pinnings of these models, and the application of these models toreal-world political and social data. Students in this class will learn to tackle research questions with regression-style analyses regardless of the distribution of the variables-of-interest. With careful introductions to logistic regression, count models, ordinal and categorical models, simulation, and out-of-sample prediction, participants will emerge witha well-rounded methodological tool kit and well-prepared for observational research of many types.

Prerequisites: A basic statistics course and familiarity with linear regression

Software: R Statistical Computing Environment

Fee: Members = $1700; Non-members = $3200

Tags: MLE, maximum likelihood estimation, GLM, Generalized Linear Model

Course Sections

Section 1

Location: University of Houston -- Houston, Texas

Date(s): May 20 - May 24

Time: 9:00 AM - 5:00 PM

Instructor(s):

  • Ling Zhu, University of Houston