Event Details

A 2 day course, 4th & 5th March 2019...

A 2 day course, 4th & 5th March 2019.

This module deals with the ubiquitous and often neglected problem of dealing with missing data, common in many types of statistical analysis. We survey some ad-hoc strategies to deal with them and show how the can lead to bias and inefficiencies. We advocate using a principled approach and the formulating of the inherent missing data mechanism.

We look at several principled methods of dealing with missing data. First we present a fully Bayesian approach using Winbugs. Secondly we create multiply imputed datasets using chained equation and then apply Rubin’s rules for combing the analyses of the models. We then do the same thing as the previous method but use multivariate techniques rather than chained equations as the method of multiple imputation.

Finally we look at examples where no imputation is needed at all. All of the methods will be illustrated through good examples using the appropriate tools for exploration and diagnostics. We will also touch on models for imputation for hierarchical models when a mixed effects.

Important please note:

We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.

Details of course fees.

Payment: Once you have registered, please pay at the online shop

https://online-payments.lancaster-university.co.uk/product-catalogue/courses/mathematics-and-statistics/short-courses-and-cpd/applied-statistics-psc-short-courses-20182019

Accommodation Details: Can be found here.

Cancellation Policy

Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.