TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course overview:
This course will cover introductory modelling for the analysis of missing data. Missing data is extremely common in all areas of science so this course will be of use to a wide variety of practitioners. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular mice package.
The course is structured over 3 days and includes classes on:
An introduction to missing data analysis terminology, missing completely at random, missing at random, not missing at random
A revision of likelihood and regression approaches
The Fully Conditional Specification (FCS) approach
An introduction to the mice package
The use of Bayesian and likelihood-based methods in missing data analysis
Bayesian missing data analysis using JAGS
More advanced missing data analysis including non-ignorable and not missing at random methods
Wednesday 8th – Classes from 09:30 to 17:30
How to run a missing data analysis in mice
• Introduction to Bayesian analysis and missing data
• The use of Bayesian and likelihood-based methods in missing data analysis
• The fully conditional specification approach to missing data analysis
Thursday 9th – Classes from 09:30 to 17:30
Including missing data in JAGS and Stan
• An introduction to the mice package
• Bayesian software tools JAGS/Stan for missing data analysis
Friday 10th – Classes from 09:30 to 17:30
Advanced missing data analysis methods
• More advanced missing data analysis including non-ignorable and not missing at random methods
• Missing data analysis in machine learning
ONLINE COURSE – Missing Data Analytics (MDAR01) This course will be delivered live
https://www.prstatistics.com/course/online-course-missing-data-analytics-mdar01-this-course-will-be-delivered-live/
Email oliverhooker@prstatistics.com
8 September 2021 - 10 September 2021
TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course overview:
This course will cover introductory modelling for the analysis of missing data. Missing data is extremely common in all areas of science so this course will be of use to a wide variety of practitioners. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular mice package.
The course is structured over 3 days and includes classes on:
An introduction to missing data analysis terminology, missing completely at random, missing at random, not missing at random
A revision of likelihood and regression approaches
The Fully Conditional Specification (FCS) approach
An introduction to the mice package
The use of Bayesian and likelihood-based methods in missing data analysis
Bayesian missing data analysis using JAGS
More advanced missing data analysis including non-ignorable and not missing at random methods
Wednesday 8th – Classes from 09:30 to 17:30
How to run a missing data analysis in mice
• Introduction to Bayesian analysis and missing data
• The use of Bayesian and likelihood-based methods in missing data analysis
• The fully conditional specification approach to missing data analysis
Thursday 9th – Classes from 09:30 to 17:30
Including missing data in JAGS and Stan
• An introduction to the mice package
• Bayesian software tools JAGS/Stan for missing data analysis
Friday 10th – Classes from 09:30 to 17:30
Advanced missing data analysis methods
• More advanced missing data analysis including non-ignorable and not missing at random methods
• Missing data analysis in machine learning