Admission requirements
A basic understanding of introductory statistical concepts and some familiarity with R as taught in Inleiding Mathematische Statistiek.
Description
An overview about each of the four topics topic presented in this course is given here below
Survival analysis (Prof. Dr. M. Fiocco)
This area of statistics deals with time to event data, whose analysis is complicated not only by
the dynamic nature of events occurring in time but also by censoring where some events are not
observed directly but it is only known that they fall in some interval or range. Different types of
censored and truncated data, non-parametric methods to estimate the survival function and
regression models to study the effect of risk factors on survival outcomes will be discussed.
Special aspects such as time-dependent covariates and stratification will be introduced.
Longitudinal data analysis (Dr. M. Signorelli)
Longitudinal data are data collected through a series of observations of the same subjects over time (“repeated measurements”). Repeated measurements from the same subject are typically correlated, so the analysis of longitudinal data cannot rely on traditional methods that assume independent and identically-distributed observations. Instead, it requires more flexible models that can account for the existing correlation between repeated measurements. Generalized linear mixed models (GLMMs) are extensions of the linear regression model that make it possible to account for such correlation. After introducing the theory behind linear and generalized linear mixed models, we will discuss how such models can be estimated, and how to use them to test statistical hypotheses and to compute predictions.
Bayesian methods (Dr. V. Masarotto).
The goal of the three lessons is to give an introduction to Bayes thinking, with some practically motivated examples. The course will provide basis knowledge of Bayesian statistics and (if possible) classic non-parametric estimation within the bayesian framework. We will explore what it means to embrace a bayesian perspective, and then move into introducing some canonical classes of models, covering simple approaches to posterior computation within such models. If time allows, we will discuss what it means to think “non-parametrically” within a Bayesian paradigm, We will include some basic estimations using the R software environment.
Online learning (Dr. D. van den Hoeven)
Online learning is a general framework that can be applied in various settings such as for example recommendation systems, auctions, games, traffic routing, statistical learning, and advertising. In online learning we work in an interactive environment. In each interaction with the environment we make a prediction, suffer the loss associated with that prediction, obtain feedback, and use that feedback to adjust our prediction for the next interaction with the environment. We will discuss various ideas and algorithms from online learning, including bandit feedback and the exponential weights algorithm.
Course Objectives
The overall aim of the course is to introduce students to four different areas of statistics. By the
end of the course, students are expected to have a basic understanding of the topics discussed
and to be able to use existing software to apply the methods covered during the course.
Timetable
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of Instruction
Weekly 2 × 45 min of lecture in class, and 2 × 45 min of practical sessions with exercises. Laptop with the statistical package R (http://www.r-project.org) already installed is required for each practical section.
Assessment method
Four individually written reports (20% each), and a presentation (20%) on a selected topic. The presentations will be held individually or in pairs, depending on the group size. The reports are regarded as practical assignments, and can not be retaken. The presentation can be retaken.
Reading list
Lecture material and references provided in class.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Please see Brightspace for more information
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.