Waseda Uni

Waseda University is a large, private university with a main campus located in Shinjuku, Tokyo, Japan. First established in 1882 as the Tōkyō Senmon Gakkō or Tōkyō College by Ōkuma Shigenobu, the school obtained university accreditation and was formally renamed as Waseda University in 1902. The university consists of 13 undergraduate schools and 23 graduate schools. Waseda is one of a select group of top 13 universities assigned additional funding under the Japanese Ministry of Education, Culture, Sports, Science and Technology’s “Top Global Universities” Project.

Waseda consistently ranks among the most academically selective and well-regarded universities in Japanese university rankings.

Courses
Course 1: Maximum Likelihood Estimation (22 hours)

Instructor: Dr Daina Chiba

Schedule: 10 – 21 September, 2018

Time:
Sep 10 – Sep 14, and Sep 18: 13:00-14:30, 14:45-16:15
Sep 19 and 20: 13:00-14:30 (Office Hours after the class)
Sep 21(Final Exam): 10:40-12:10 (A lunch gathering after the exam)

Location: Waseda campus, Waseda University, Japan

Tuition Fees:
£200 Internal WASEDA applicants
£300 External Institution applicants

To apply: Please complete the following online Application form choosing ‘4A’ from the course selection menu. Application deadline 31 August.

Course 2: Survival Analysis (22 hours)

Instructor: Alejandro Quiroz Flores

Schedule: 10 – 21 September, 2018

Time:
Sep 10 – Sep 14, and Sep 18: 13:00-14:30, 14:45-16:15
Sep 19 and 20: 13:00-14:30 (Office Hours after the class)
Sep 21(Final Exam): 10:40-12:10 (A lunch gathering after the exam)

Location: Waseda campus, Waseda University, Japan

Tuition Fees:
£200 Internal WASEDA applicants
£300 External Institution applicants

To apply: Please complete the following online Application form choosing ‘4B’ from the course selection menu. Application deadline 31 August.

Please Note:
• Students need to make lodging and travel arrangements on their own;
• Waseda cannot sponsor student visa.

COURSE DESCRIPTIONS

Maximum Likelihood Estimation (10 – 21 September, 2018; 22 hours)

Instructor
Daina Chiba is a Senior Lecturer in the Department of Government at the University of Essex. A graduate of Rice University, he completed his postdoctoral fellowship at Duke University. His research interests encompass the areas of militarized conflict, international institutions, and political methodology. His work has appeared in Political Analysis, American Journal of Political Science, Journal of Politics, Political Science Research and Methods, Journal of Conflict Resolution, and Journal of Peace Research.

Course Content
In this course, students will learn how to build a statistical model to explain the variation of a categorical (binary, ordinal, nominal) dependent variable. They will learn how to build statistical models by properly specifying a likelihood function appropriate to their theory and data. They will then learn how to estimate the unknown parameters of these models using maximum likelihood estimation and how to produce measures of uncertainty (standard errors). Next, they will learn how to use the estimates of the parameters of the model to interpret its substantive implications mainly by calculating substantive effects of the form “my estimates suggest an additional year of education would increase an individual’s chance of turning out to vote by 3%.” Finally, students will learn how to use simulation techniques to put confidence intervals around these substantive effects of the form “my estimates suggest an additional year of education would increase an individual’s chance of turning out to vote by 3%, plus or minus 1%.” Throughout the course there will be an emphasis on how to best describe and explain the models they build and how best to communicate substantive implications to a broad academic audience.

The foundation of building a statistical model is proper development of a likelihood function and that requires an understanding of probability distributions. Thus, we will start with a brief introduction to probability theory at a level appropriate for students with no background in probability theory. The specific models we will subsequently cover are the Bernoulli-logistic model (logit), the normal-linear model (regression), ordered logit, multinomial logit, and event count models (e.g., Poisson, negative binomial).

Objectives
After finishing this course students should be able to use a wide variety of statistical models in their own work, understand the underlying assumptions of these models, be able to explain the ways in which the models are appropriate or not for the theory and data at hand, and to develop and interpret the substantive implications of the statistical estimates produced by these models.

Prerequisites
The course should be taken subsequent to a course on linear regression using OLS. Knowledge of basic calculus will be useful — though not strictly essential. No matrix algebra will be required. That said, statistical models are mathematical models and so we will use a lot of basic algebra and mathematical notation in order to formalize our theoretical intuitions into mathematical (statistical) models. Students should be ready to consume and produce models presented in this way.

Representative Background Reading
Gary King. 1998. Unifying Political Methodology. University of Michigan Press

Statistical Software
R

Survival Analysis (10 – 21 September, 2018; 22 hours)

Instructor
Alejandro Quiroz Flores is Senior Lecturer (Associate Professor) at the Department of Government, University of Essex. He obtained his PhD in Politics at New York University. He specializes in Methodology and Political Economy. His work has appeared at Political Science Research and Methods, the British Journal of Political Science, and International Studies Quarterly, among others. He is the author of Ministerial Survival During Political and Cabinet Change: Foreign Affairs, Diplomacy and War, by Routledge (2016).

Course content

This course will cover the statistical concepts and techniques that are used to model time. These models are also known as survival or event history models, and we use them to analyze the duration of time until some event happens—the termination of civil wars, the completion of a medical treatment, or the loss of political office, among other events.

The course will be divided into three main sections:

1. Continuous Time Duration Models: We will examine parametric duration models (exponential, Weibull, log-logistic, generalized gamma, etc.) and semi-parametric duration models (Cox model). In addition to seeing how these models are estimated and interpreted, we will also look at various residual-based diagnostic tests.

2. Discrete Time Duration Models: We will look at the connection between discrete time duration models and binary time-series-cross-section models. We will examine various ways to deal with time dependence, ongoing events, multiple events, and time varying covariates. Finally, we will take a look at Markov transition models.

3. More Advanced Duration Models: We will examine models dealing with competing risks, split populations, heterogeneity, frailty, and repeated events, as well as dyadic event history models for emulation and machine learning applications to survival analysis.

Objectives

The central objective of this course is to learn how to identify, and correctly apply, the statistical techniques appropriate to answering questions relating to time and duration. Students will be able to identify and classify data problems in survival analysis, define the appropriate survival function, distribution function, hazard function, relative hazard, and cumulative hazard, as well as summarize and interpret analyses of survival data using various estimators. By the end of the course, students should be quite adept at using Stata to estimate and interpret a wide variety of different duration models.

Prerequisites

Students should already have some experience with Maximum Likelihood Estimation. Some knowledge of basic calculus (differentiation and integration), exponents and logarithms, and Stata code will be helpful.

Representative Background Reading

Box-Steffensmeier, Janet, and Bradford S. Jones. 2004. Event History Modeling: A Guide for Social Scientists. New York: Cambridge University Press